SINGLE NUCLEOTIDE POLYMORPHISMS ASSOCIATED WITH DIETARY WEIGHT LOSS

- DSM IP ASSETS B.V.

The present invention relates to genetic polymorphisms associated with obesity and obesity-related phenotypes and their use in predicting if an individual successfully completes a dietary weight loss intervention program.

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Description
FIELD OF THE INVENTION

The present invention is in the field of obesity. More in particular it relates to genetic polymorphisms and their effect on dietary weight loss intervention programs. Moreover, the present invention pertains to genetic tests and methods using the polymorphisms, particularly methods to predict an obese individual's likelihood to complete a dietary weight loss intervention program successfully.

BACKGROUND OF THE INVENTION

Obesity is a worldwide epidemic found across all age groups. Especially in industrialized countries, it has increased at a fast rate over the past two decades and is now a worldwide leading public health problem. For example, while in 1996 26% adult Americans were overweight and 10% severely so, currently more than 65% are overweight, with nearly 31% meeting the criteria for obesity. As obesity portends an epidemic of related chronic diseases such as type-2 diabetes, hypertension and cardiovascular events, people with obesity especially people with extreme obesity are at risk for many health problems. The economic cost attributable to obesity in the United States alone has been estimated to be as high as $100 billion per year and includes not only direct health care costs but also the cost of lost productivity in affected individuals.

While diet and lifestyle contribute to obesity and the trend of decreased physical activity and increased caloric intake is probably responsible for the recent rise in obesity, it is important to understand that genetics plays a key role. Each individual's genetic background remains an important determinant of susceptibility to obesity. For instance, half of the population variation in body mass index (BMI), a common measure of obesity, is determined by inherited factors.

Many studies have reported that common genetic variants, usually single nucleotide polymorphisms (SNPs), are associated with an increased risk for obesity. Two approaches have been used to date to find these variants, linkage analysis and association studies. Although on the basis of linkage studies some regions have been repeatedly implicated to play a role in obesity, no genes have been found in these regions that have been seen to contribute to the disease. By using association studies several associations between obesity or obesity-related traits and common genetic variants have been reported. Unfortunately, many of the reported associations have not been consistently replicated.

Altering dietary habits is the cornerstone of weight loss intervention programs for overweight and obese patients. As it is unlikely that all overweight or obese individuals can lose weight with a standard protocol, dietary guidance should be individualized to allow for personalized approaches and recommendations and to increase success rates in these programs. Despite the increasing knowledge of loci and genes associated with obesity and obesity-related traits, no useful genetic variants exist on the basis of which dietary weight loss intervention programs can be tailored for overweight or obese individuals. A recent study even drew the conclusion that common SNPs in a panel of obesity-related candidate genes play a minor role, if any, in modulating weight changes induced by certain diets (see Sørensen et al., 2006). Given that no predictive genetic information about the response to diet is available and dietary treatment of obesity could be dramatically improved if predictive genetic information about the genetic response to diet was available, there is a clear need in the art for identifying genetic variants that predict the response of an overweight or obese individual to a dietary weight loss intervention program, for instance SNPs that predict the likelihood that an overweight or obese individual successfully completes such a program. The present invention meets these needs.

SUMMARY OF THE INVENTION

It was found in accordance with the present invention that markers exist that are associated with weight loss. The markers can be used to predict the likelihood that an individual, such as an overweight or obese individual, is successful in a dietary weight loss intervention program. Successful in this respect inter alia means that the individual successfully completes the dietary weight loss intervention program, e.g. the individual loses the target amount of weight and/or fat mass. Loss of a target amount of weight and/or fat mass can be accomplished by following e.g. a hypo-caloric diet. So, successful completion can be the consequence of the choice of diet. An individual may benefit more from one diet compared to another diet (e.g. a high fat/low carbohydrate hypo-caloric diet compared to a low fat/high carbohydrate hypo-caloric diet or vice versa). Markers provided herein can also be used to determine the optimal diet for an individual. The term “associated with” in connection with the relationship between a genetic characteristic, e.g., a marker, allelic variant, haplotype, or polymorphism, and a trait means that there is a statistically significant level of relatedness between them based on any generally accepted statistical measure of relatedness. Those skilled in the art are familiar with selecting an appropriate statistical measure for a particular experimental situation or data set. Examples of suitable statistical methods are described herein. Accordingly, the present invention is directed to methods wherein use is made of the genetic characteristics in predicting likely response, preferably likely successful response, to weight loss and weight management. The invention also provides kits for use in the methods and uses of the present invention, e.g. kits to determine whether an individual is likely to successfully complete a specific diet on the basis of analysis of genetic markers e.g. SNPs. The markers can for instance be found in genes associated with overweight, obesity or obesity-related metabolic traits. The resulting information can be used to classify individuals such as overweight or obese individuals based on their genetic tendency to have success with certain types of diet. This will help professionals in the field of weight management to improve targeting these individuals with appropriate (nutritional) advice regarding their weight management. As a result thereof, the success rate of dietary weight loss intervention programs will increase.

DETAILED DESCRIPTION OF THE INVENTION

The invention is based on the finding that single nucleotide polymorphisms (SNPs) selected from the group of SNP markers as set forth in Table 1 are associated with weight loss. They can be used to predict the likelihood of success of an individual, preferably an overweight or obese individual, in a dietary weight loss intervention program. If a SNP selected from the group of SNP markers as set forth in Table 1, e.g. SNP rs183766 (SEQ ID NO:17), is identified, the likelihood of success of an individual, preferably an overweight or obese individual, in a dietary weight loss intervention program is higher than when the SNP is not identified. Preferably, the program comprises administration of a hypo-caloric diet. Furthermore, the invention provides SNPs which can be used to determine the type of diet an individual, preferably an overweight or obese individual, is most likely to complete successfully. These SNPs are shown in Tables 2 and 3. Again the diet may be part of a dietary weight loss intervention program. On the basis of SNPs identified in the present invention individuals can be classified into individuals that have a higher tendency to successfully complete dietary weight loss intervention programs comprising low fat/high carbohydrate diets than dietary weight loss intervention programs comprising high fat/low carbohydrate diets, and into individuals that have a higher tendency to successfully complete dietary weight loss intervention programs comprising high fat/low carbohydrate diets than dietary weight loss intervention programs comprising low fat/high carbohydrate diets. Moreover, the invention is based on the finding that SNPs selected from the group of SNP markers as set forth in Table 4 are associated with weight loss in females. They can be used to predict the likelihood of success of a female individual, preferably an overweight or obese female individual, in a dietary weight loss intervention program. If a SNP selected from the group of SNP markers as set forth in Table 4, e.g. SNP rs183766 (SEQ ID NO:17), is identified in a female, the likelihood of success of a female individual, preferably an overweight or obese female individual, in a dietary weight loss intervention program is higher than when the SNP is not identified. Preferably, the program comprises administration of a hypo-caloric diet. The invention thus also provides polymorphisms that are useful in predicting the outcome of weight loss intervention programs, particularly programs having a component of dietary intervention e.g. diets. The present invention is directed to methods capable of predicting likely response, preferably likely successful response, to weight loss and weight management based on genetic polymorphisms and methods to assess an individual's likelihood of responsiveness to weight management programs by genetically classifying individuals as likely susceptible or likely resistant to weight management programs, e.g. weight management programs comprising a dietary intervention. The results as found herein indicate that individuals carrying certain polymorphisms have great difficulty in managing their weight and further shows that the polymorphisms can predict the outcome of body weight reduction strategies that are based on dietary intervention such as diets. Consequently, the identification of the polymorphisms can help weight management professionals to design alternative weight management programs for these individuals.

One or more the polymorphisms may be part of a haplotype which may have an association link with the likelihood of an individual to successfully or unsuccessfully complete a certain dietary weight loss intervention program. As used herein, “haplotype” refers to a set of alleles found at linked polymorphic sites on a single chromosome. The linked sites may include part of a gene, an entire gene, several genes, or a region devoid of genes (but which perhaps contains a DNA sequence that regulates the function of nearby genes). The haplotype preserves information about the phase of the polymorphic nucleotides, that is, which set of variances were inherited from one parent (and are therefore on one chromosome) and which from the other. In a preferred embodiment the programs comprise dietary intervention either alone or as a major component. Next to suitable diets, i.e. personalized diets based on the genetic profile of an individual, weight loss intervention programs may however also include other components such as e.g. drug treatment, surgical treatment e.g. liposuction, behavioural therapy, increase in physical activity and dietary supplement treatment.

As on the basis of the genetic markers according to the present invention, in particular the genetic markers as shown in Tables 2 and 3, individuals such as overweight or obese individuals may be identified that have an increased likelihood to successfully complete a dietary weight loss intervention program comprising a low fat/high carbohydrate diet compared to a dietary weight loss intervention program comprising a high fat/low carbohydrate diet and vice versa. The identification of SNPs in an individual that are associated with specific types of diets can help weight management professionals to design suitable dietary weight loss intervention programs for these individuals.

In a first aspect the invention relates to the use of at least one single nucleotide polymorphism (SNP) selected from the group of SNP markers as set forth in Tables 1 to 4 for predicting the likelihood of success of an individual in a dietary weight loss intervention program. In a preferred embodiment the SNP marker is rs183766 (SEQ ID NO:17). The individual may be overweight or obese. The SNP may be selected from the group of SNP markers as set forth in Table 4, preferably the SNP marker is rs183766 (SEQ ID NO:17), and the individual may be a female. The dietary weight loss intervention program may comprise subjecting the individual to a hypo-caloric diet. The invention also relates to the use of at least one single nucleotide polymorphism (SNP) selected from the group of SNP markers as set forth in Tables 2 and 3 for determining a diet an individual is most likely to complete successfully. The diet may be a high fat/low carbohydrate or a low fat/high carbohydrate diet. The diet may be a hypo-caloric diet.

In an aspect the invention relates to the use of at least one genetic marker such as a polymorphism, e.g. a SNP, selected from the group of markers as set forth in Table 1, preferably SNP marker rs183766 (SEQ ID NO:17), for predicting the likelihood of success of an individual in a dietary weight loss intervention program. In other words, the data provided herein show that a correlation, association, linkage or other relation between a specific marker and the likelihood of success in a dietary weight loss intervention program can be established. Preferably, the program includes administration of a hypo-caloric diet to the individual.

In another aspect the invention relates to the use of at least one genetic marker such as a polymorphism, e.g. a SNP, selected from the group of markers as set forth in Table 4, preferably SNP marker rs183766 (SEQ ID NO:17), for predicting the likelihood of success of a female individual in a dietary weight loss intervention program. In other words, the data provided herein show that a correlation, association, linkage or other relation between a specific marker and the likelihood of success in a dietary weight loss intervention program can be established. Preferably, the program includes administration of a hypo-caloric diet to the individual.

In a further aspect the invention relates to the use of at least one genetic marker such as a polymorphism, e.g. a SNP, selected from the group of markers as set forth in Tables 2 and 3 for determining a diet an individual is most likely to complete successfully in a dietary weight loss intervention program. The dietary weight loss intervention programs comprise treatment of an individual with a diet, e.g. a hypo-caloric diet. The marker might determine the likelihood that an individual successfully or unsuccessfully completes a dietary component of the intervention program such as a diet. Diets used in dietary weight loss intervention programs designed to treat individuals are well known to the skilled person. These include, but are not limited to, low energy/low calorie diets. Preferred diets in the light of the present invention include, but are not limited to, high fat/low carbohydrate diets or low fat/high carbohydrate diets. The high fat/low carbohydrate or low fat/high carbohydrate diets may be hypo-energetic diets (hypo-caloric diets). In an embodiment the individual is overweight or obese. An “individual” as used in the present application refers to a human.

An “overweight individual”, as used herein, refers to an individual fulfilling the normal definition of overweight individual as defined by the medical knowledge at the time of diagnosis. Useful criteria for defining an individual as overweight include, but are not limited to, a body mass index (BMI) of 25-29.9, male individual with a waist measurement greater than 40 inches (102 cm), female individual with a waist measurement greater than 35 inches (88 cm), and all individuals with a waist-to-hip ratio of 1.0 or higher. An “obese individual”, as used herein, refers to an individual fulfilling the normal definition of obese individuals as defined by the medical knowledge at the time of diagnosis. Useful criteria for defining an individual as obese include, but are not limited to, a body mass index (BMI) of 30 or higher. The definitions for overweight or obese can vary in children or teenagers. The definitions are definitions at the time of observation of the individual in the light of the then current medical knowledge. The definitions may thus change.

A “hypo-energetic (hypo-caloric) diet” as used herein means a diet wherein the daily energy intake is less than the daily energy requirement, e.g. a diet with an energy deficiency of at least 100, 200, 300, 400, 600, 800, 1000, 1200, 1500 or 2000 kcal/day. “High fat” diets as used herein means diets having at least 30%, preferably at least 40%, more preferably 40-45% of energy from fat. “Low fat” diets as used herein means diets having less than 30%, preferably less than 25%, more preferably 20-25% of energy from fat. “Low carbohydrate” diets as used herein means diets having less than 50%, preferably less than 45%, more preferably 40-45% of energy from carbohydrate. “High carbohydrate” diets as used herein means diets having at least 50%, preferably at least 60%, more preferably 60-65% of energy from carbohydrate. The diets may further contain other components such as e.g. proteins. The diets may have e.g. 15% of energy from proteins. Preferably, the individuals on the dietary intervention program do not consume alcohol. Where exclusion of alcohol is not possible, intake should be minimal, with an upper limit of two glasses (2×150 ml) in total. Energy from alcohol should be subtracted from total energy intake and thereafter macronutrient intake should be calculated on the remaining energy. Where possible, viscous soluble fibres should be avoided in the diets, since they are thought to have the greatest impact on glucose and lipid metabolism (e.g. oats and guar gum). Furthermore, it may be attempted to standardise other sources of soluble fibre within the diets (e.g. fruit and vegetables, especially legumes). Individuals participating in dietary intervention programs may be encouraged to consume equal amounts of polyunsaturated, mono-unsaturated and saturated fats by ensuring incorporation of olive oil (or equivalent) and sunflower oil (or equivalent) into each day's choices (in addition to saturated fat predominately from meat and dairy products). They may avoid using food products including specialist margarines which contain added plant sterols, omega-3 fatty acids or soy compounds, and soy based products. Furthermore, they may be encouraged to consume oily fish at least once a week within the fat restriction of the diet and they may attempt to maintain comparable ratios of simple sugars to complex carbohydrates. Individuals who are already taking vitamin and mineral supplementation before starting the dietary intervention program may continue taking the same dose throughout the program and this intake may be included in the intake analysis.

In an embodiment of the invention the marker is present in a locus, gene or gene cluster associated with an obesity-related phenotype. As used herein, “phenotype” refers to any observable or otherwise measurable physiological, morphological, biological, biochemical or clinical characteristic of an individual.

In a preferred embodiment of the invention the SNP marker is rs183766 (SEQ ID NO:17). The marker is present within the SV2C gene. The SV2C gene encodes a synaptic vesicle glycoprotein, primarily expressed in old brain regions, particularly pallidum, substantia nigra, midbrain, brainstem and olfactory bulb. SV2C is involved in regulated insulin secretion and control of glucose-evoked granule recruitment. As shown herein, rs183766 (SEQ ID NO:17) is significantly associated with weight loss in different analyses. The best result with this SNP marker was achieved using the additive model, the most commonly applied model in genetic association analyses, with a p value of 1.91*10−6. In addition, the relatively high minor allele frequency (34.7%) of this particular SNP marker makes it a valuable predictive marker, i.e. the allele associated with weight loss is found in up to 60% of the population. In addition to the very significant p values achieved in different analyses with the SNP marker rs183766 (SEQ ID NO:17), the present study has shown that the SV2C gene comprises two additional unrelated SNP markers which are significantly associated with weight loss.

Of course, a combination of markers can be used in the methods, kits, uses, etc of the present invention. Preferably, the markers are selected from SNP markers as set forth in Tables 1 to 4. Preferably, one of the markers is SNP marker rs183766 (SEQ ID NO:17). Preferred markers used in combination with SNP marker rs183766 (SEQ ID NO:17) are selected from the group consisting of rs7230740 (SEQ ID NO:29), rs751539 (SEQ ID NO:27), rs8047814 (SEQ ID NO:32), rs2084635 (SEQ ID NO:33), rs655970 (SEQ ID NO:3), rs621750 (SEQ ID NO:2), rs11073977 (SEQ ID NO:34), rs11679740 (SEQ ID NO:11), rs3755264 (SEQ ID NO:5), rs3771138 (SEQ ID NO:4), rs203142 (SEQ ID NO:18), rs6751402 (SEQ ID NO:13), rs6958502 (SEQ ID NO:19), rs505922 (SEQ ID NO:25), rs2247380 (SEQ ID NO:12), rs7230740 (SEQ ID NO:29), rs12466364 (SEQ ID NO:10), rs963103 (SEQ ID NO:30), rs6442037 (SEQ ID NO:15), rs1019019 (SEQ ID NO:21), rs928571 (SEQ ID NO:35), rs247979 (SEQ ID NO:36), rs11719455 (SEQ ID NO:16), rs4890647 (SEQ ID NO:37), rs657152 (SEQ ID NO:24), rs928534 (SEQ ID NO:38), rs11058150 (SEQ ID NO:39), rs3779341 (SEQ ID NO:20), rs11684785 (SEQ ID NO:40), rs6750788 (SEQ ID NO:41), rs1471910 (SEQ ID NO:42), rs6434276 (SEQ ID NO:43), rs7831030 (SEQ ID NO:22), rs10899257 (SEQ ID NO:44), rs876614 (SEQ ID NO:6), rs920965 (SEQ ID NO:23), rs2043448 (SEQ ID NO:45), rs1515241 (SEQ ID NO:46), rs12619445 (SEQ ID NO:47), rs2130858 (SEQ ID NO:48), rs6718009 (SEQ ID NO:49), rs4492387 (SEQ ID NO:50), rs11733026 (SEQ ID NO:51), rs1109425 (SEQ ID NO:52), rs3753472 (SEQ ID NO:53), rs7355583 (SEQ ID NO:54), rs7700981 (SEQ ID NO:55), rs10140366 (SEQ ID NO:56), rs13220420 (SEQ ID NO:57), rs6010669 (SEQ ID NO:58), rs2428514 (SEQ ID NO:59), rs3731612 (SEQ ID NO:60), rs10818023 (SEQ ID NO:61), rs4691707 (SEQ ID NO:62), rs17407330 (SEQ ID NO:63), rs1507081 (SEQ ID NO:64), rs12828607 (SEQ ID NO:65), rs12511535 (SEQ ID NO:66) and any combination thereof. Markers may be present in coding (exons) but may also be present in non-coding regions (intron and intergenic regions). They may be present in different genes e.g. one marker in a first gene and another marker in a second gene. If more than one marker is used, the markers may be in linkage disequilibrium with one another, preferably in non-tight linkage disequilibrium. “Linkage disequilibrium” or “allelic association” means the preferential association of a particular allele or genetic marker with a specific allele or genetic marker at a nearby chromosomal location more frequently than expected by chance for any particular allele frequency in the population. Linkage disequilibrium may result from natural selection of certain combination of alleles or because an allele has been introduced into a population too recently to have reached equilibrium (random association) between linked alleles. A marker in linkage disequilibrium with disease predisposing variants can be particularly useful in detecting susceptibility to disease (or association with sub-clinical phenotypes), notwithstanding that the marker does not cause the phenotype. Methods to determine linkage disequilibrium are well known to the skilled artisan. The present invention thus also pertains to methods and uses comprising determining in vitro the genotype of an SNP presented in one of the Tables 1 to 4, and/or at least one other SNP, e.g. another SNP presented in one of the Tables 1 to 4, in DNA taken from an individual. This other SNP may be in linkage disequilibrium with the first SNP.

Obesity-related phenotypes include, but are not limited to, body weight, BMI, percent fat mass, and serum triglycerides, cholesterol, and glucose, to name just a few. Genes associated with these phenotypes have been found (see Obesity: Genomics and postgenomics, Eds: Clement and Sørensen, Informa Healthcase, first edition, 2007). In a preferred embodiment the marker e.g. SNP is shown in one of the Tables 1 to 4. It is to be understood that any marker that is in linkage disequilibrium with any of the SNPs shown in one of the Tables 1 to 4 can also be used in the various aspects and embodiments of the present invention. These markers do not necessarily have to be present in the same locus, gene or gene cluster as the markers shown in one of the Tables 1 to 4. They may be part of other more distant genes. However, they should be in linkage. “Linkage” describes the tendency of genes, alleles, loci or genetic markers to be inherited together as a result of their location on the same chromosome, and can be measured by percent recombination between the two genes, alleles, loci or genetic markers that are physically-linked on the same chromosome. Linkage disequilibrium can be determined in terms of r2 which is the correlation coefficient and/or d which is the genetic distance. At least one of them should be above 0.8. Some linked markers occur within the same gene or gene cluster.

In a further aspect, the invention pertains to the use of at least one marker shown in one of the Tables 2 and 3 for determining a diet an individual is most likely to complete successfully. In other words, the marker may be used for selecting an optimal diet for an individual. “Optimal” means, among others, that the individual should remain on the diet and complete it successfully e.g. should lose at least the target amount of weight and/or fat mass. On the basis of a correlation, association, linkage or other relation between a genetic marker and the likelihood to remain on and successfully complete a specific diet, a suitable diet can be communicated, prescribed, suggested and/or recommended to an individual and/or added to an individual's food or diet. Preferably, the marker is a genetic marker such as a polymorphism, e.g. a SNP. From the genetic markers as shown herein markers can be selected the presence of which are indicative of an increased likelihood of an individual to successfully complete a low fat/high carbohydrate diet compared to a high fat/low carbohydrate diet and markers can be selected the presence of which are indicative of an increased likelihood of an individual to successfully complete a high fat/low carbohydrate diet compared to a low fat/high carbohydrate diet.

As used herein “polymorphism” refers to DNA sequence variation in the cellular genome of an individual, typically with a population frequency of more than 1%. A polymorphic marker or site is the locus at which genetic variation occurs. Preferred markers have at least two alleles, each occurring at frequency of greater than 1%, and more preferably greater than 10% or 20% of a selected population. A polymorphic locus may be as small as one base pair. Polymorphic markers include restriction fragment length polymorphisms, variable number of tandem repeats, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, and insertion elements such as Alu. The first identified allelic form is arbitrarily designated as the reference form and other allelic forms are designated as alternative or variant alleles. The allelic form occurring most frequently in a selected population is sometimes referred to as the wild-type form. Diploid organisms may be homozygous or heterozygous for allelic forms. A SNP occurs at a polymorphic site occupied by a single nucleotide. A SNP usually arises due to substitution of one nucleotide for another at the polymorphic site, but it can also arise from an insertion or deletion of a nucleotide relative to a reference allele.

The invention also pertains to a method for predicting the likelihood of success of an individual in a dietary weight loss intervention program, the method comprising the steps of obtaining a biological sample comprising nucleic acid of the individual and genotyping the nucleic acid for at least one single nucleotide polymorphism (SNP) selected from the group of SNP markers as set forth in Tables 1 to 4, wherein the presence of at least one SNP marker as set forth in Tables 1 to 4 is indicative of an increased likelihood of success of an individual in a dietary weight loss intervention program. In a preferred embodiment the SNP marker is rs183766 (SEQ ID NO:17). The individual may be overweight or obese. The dietary weight loss intervention program may comprise subjecting the individual to a hypo-caloric diet. The SNP may be selected from the group of SNP markers as set forth in Table 4, preferably the SNP marker is rs183766 (SEQ ID NO:17), and the individual may be a female. The SNP may also be selected from the group of SNP markers as set forth in Table 2 and the dietary weight loss intervention program may comprise subjecting the individual to a low fat/high carbohydrate diet. The SNP may also be selected from the group of SNP markers as set forth in Table 3 and the dietary weight loss intervention program may comprise subjecting the individual to a high fat/low carbohydrate diet.

The present invention also provides a method for predicting the likelihood of success of an individual in a dietary weight loss intervention program, the method comprising the steps of a) obtaining a biological sample comprising nucleic acid of the individual, and b) genotyping the nucleic acid for at least one single nucleotide polymorphism (SNP) selected from the group of SNP markers as set forth in Table 1. In a preferred embodiment the SNP marker is rs183766 (SEQ ID NO:17). The dietary weight loss intervention program may comprise a high fat/low carbohydrate or a low fat/high carbohydrate diet. Preferably, the program comprises a hypo-caloric diet.

The present invention furthermore provides a method of determining whether an individual has an increased likelihood to successfully complete a specific diet, the method comprising the step of a) obtaining a biological sample comprising nucleic acid of the individual, and b) genotyping the nucleic acid for at least one single nucleotide polymorphism (SNP) selected from the group of SNP markers as set forth in Tables 2 and 3. SNPs in Table 2 are associated with an increased likelihood of the individual to successfully complete a low fat/high carbohydrate diet, while SNPs in Table 3 are associated with an increased likelihood of the individual to successfully complete a high fat/low carbohydrate diet. The SNPs can be used in the method of determining whether an individual has an increased predisposition to complete a high fat/low carbohydrate diet or a low fat/high carbohydrate diet. The diets used in the methods and uses of the present invention are preferably hypo-energetic. The individuals may be overweight or obese.

The present invention also provides a method for predicting the likelihood of success of a female individual in a dietary weight loss intervention program, the method comprising the steps of a) obtaining a biological sample comprising nucleic acid of the female individual, and b) genotyping the nucleic acid for at least one single nucleotide polymorphism (SNP) selected from the group of SNP markers as set forth in Table 4. In a preferred embodiment the SNP marker is rs183766 (SEQ ID NO:17). The dietary weight loss intervention program may comprise a high fat/low carbohydrate or a low fat/high carbohydrate diet. Preferably, the program comprises a hypo-caloric diet.

In the methods and uses of the present invention the occurrence of a specific allelic form (e.g. A allelic form) of a SNP may be assessed by contacting a nucleic acid derived from the genome of an individual with a first oligonucleotide that anneals with higher stringency with the specific allelic form (e.g. A allelic form) of the polymorphism than with another allelic form (e.g. T allelic form) of the polymorphism and assessing annealing of the first oligonucleotide and the nucleic acid, whereby annealing of the first oligonucleotide and the nucleic acid is an indication that the genome of the individual comprises the specific allelic form (e.g. A allelic form) of the polymorphism. The method may be extended by assessing the occurrence of the other allelic form (e.g. T allelic form) of the polymorphism by contacting the nucleic acid with a second oligonucleotide that anneals with higher stringency with the other allelic form (e.g. T allelic form) of the polymorphism than with the specific allelic form (e.g. A allelic form) of the polymorphism and assessing annealing of the second oligonucleotide and the nucleic acid, whereby annealing of the second oligonucleotide and the nucleic acid is an indication that at least one allele of the respective gene in the genome of the individual does not comprise the specific allelic form (e.g. A allelic form) of the polymorphism. The first and second oligonucleotides may be attached to a support. The support may be the same for both oligonucleotides.

“Biological sample” as used in the present invention encompasses a variety of sample types which can be used as source material for isolating nucleic acids. They include, but are not limited to, solid materials (e.g., tissue, tissue cultures or cells derived there from and the progeny thereof, hair follicle samples, biopsy specimens, buccal cells provided by a swab, skin and nose samples) and biological fluids (e.g. urine, faecal material, blood, semen, amniotic fluid, tears, saliva, sputum, sweat, mouth wash). Any biological sample from a human individual comprising even one cell comprising nucleic acid can be used in the methods of the present invention. The term also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents, solubilisation, or enrichment for certain components, such as proteins or polynucleotides. The methods and uses of the present invention are preferably conducted on a sample that has previously been removed from the individual and do preferably not involve diagnosis practiced on the human body.

Nucleic acid molecules as used herein refers to polymeric forms of nucleotides and includes both sense and antisense strands of RNA, cDNA, genomic DNA, and synthetic forms and mixed polymers of the above, with genomic DNA being preferred. A nucleotide refers to a ribonucleotide, deoxy(ribo)nucleotide or a modified form of either type of nucleotide. The term also includes single- and double-stranded forms of DNA. In addition, a polynucleotide may include either or both naturally-occurring and modified nucleotides linked together by naturally-occurring and/or non-naturally occurring nucleotide linkages. The nucleic acid molecules may be modified chemically or biochemically or may contain non-natural or derivatized nucleotide bases, as will be readily appreciated by those of skill in the art. Also included are synthetic molecules that mimic polynucleotides in their ability to bind to a designated sequence via hydrogen bonding and other chemical interactions. Such molecules are known in the art and include, for example, those in which peptide linkages substitute for phosphate linkages in the backbone of the molecule. A reference to a nucleic acid sequence encompasses its complement unless otherwise specified. Thus, a reference to a nucleic acid molecule having a particular sequence should be understood to encompass its complementary strand, with its complementary sequence. The complementary strand is also useful, e.g., for antisense therapy, hybridization probes and PCR primers.

Nucleic acids can be isolated from a particular biological sample using any of a number of procedures, which are well-known in the art, the particular isolation procedure chosen being appropriate for the particular biological sample. Methods of isolating and analyzing nucleic acid variants as described above are well known to one skilled in the art and can be found, for example in the Molecular Cloning: A Laboratory Manual, 3rd Ed., Sambrook and Russel, Cold Spring Harbor Laboratory Press, 2001 and Current Protocols in Molecular Biology Volumes I-III, 4th edition, Ausubel et al., John Wiley and Sons, 1995. Many of the methods require amplification of nucleic acid from target samples. This can be accomplished by techniques such as e.g. PCR, ligase chain reaction, nucleic acid based sequence amplification, self-sustained sequence replication and transcription amplification. Genetic markers such as the SNPs can be detected from the isolated nucleic acids using techniques including DNA hybridization methods (e.g. Southern Blotting, FISH), direct sequencing with radioactively, enzymatically, luminescently or fluorescently labelled primers (manually or automated), restriction fragment length polymorphism (RFLP) analysis, heteroduplex analysis, single strand conformational polymorphism (SSCP) analysis, denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), use of linked genetic markers, mass spectrometry e.g. MALDI-TOF, and chemical cleavage analysis to name just a few. Of course DNA MicroArray technology suitable for detecting genetic markers such as SNPs can also be used. All methods are explained in detail, for example, in the Molecular Cloning: A Laboratory Manual, 3rd Ed., Sambrook and Russel, Cold Spring Harbor Laboratory Press, 2001.

Primers used may be oligonucleotides hybridizing specifically with one allele. They are called allele-specific oligonucleotides. In the allele-specific PCR methodology, a target DNA is preferentially amplified only if it is completely complementary to the 3′-terminus of a specific PCR amplification primer. The 3′-terminus of the primer is designed so as to terminate at, or within one or two nucleotides of a known mutation site within the target DNA to which it possesses a complementary sequence. Under the appropriate reaction conditions, the target DNA is not amplified if there is a single nucleotide mismatch (e.g., a nucleotide substitution caused by a mutation) or a small deletion or insertion, at the 3′-terminus of the primer. Accordingly, allele-specific PCR may be utilized to detect either the presence or absence of (at least) a single nucleotide mismatch between the primer sequence (which is complementary to a pre-selected target sequence) and a nucleic acid within the sample. Amplification of the target sequence is indicative of a lack of even a single mismatched nucleotide. The markers in the present invention are preferably analyzed using methods amenable for automation. Primer extension analysis can be performed using any method known to one skilled in the art. Oligonucleotides, probes and/or primers may be naturally occurring or synthetic, but are typically prepared by synthetic means. They may be immobilized on a solid support. For instance, oligonucleotides, probes and/or primers as described herein can be used as a DNA chip. The chip may contain a primer corresponding to a single allelic form of a marker but may also contain a primer corresponding to both allelic forms of a marker. It may even comprise primers for different markers. The appropriate length of an oligonucleotide, probe and/or primer depends on its intended use but typically ranges from 10 to 75, preferably 15 to 40 nucleotides. Short primer molecules generally require cooler temperatures to form sufficiently stable hybrid complexes with the template. A primer need not reflect the exact sequence of the template but must be sufficiently complementary to hybridize with a template. Conditions suitable for hybridization are generally known in the art and will be apparent to the skilled artisan. A non-limiting example of stringent hybridization conditions is hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 50-65° C. Stringent conditions can for instance be found in Molecular Cloning: A Laboratory Manual, 3rd Ed., Sambrook and Russel, Cold Spring Harbor Laboratory Press, 2001 and Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1-6.3.6. The term “primer site” refers to the area of the target DNA to which a primer hybridizes. The term “primer pair” means a set of primers including a 5′-upstream primer that hybridizes with the 5′-end of the DNA sequence to be amplified and a 3′-downstream primer that hybridizes with the complement of the 3′-end of the sequence to be amplified.

As used herein, “genotype” refers to the genetic constitution of an individual. More specifically, “genotyping” as used herein refers to the analysis of DNA in a sample obtained from a subject to determine the DNA sequence in a specific region of the genome, e.g. a locus that influences a trait. It may refer to the determination of DNA sequence at one or more polymorphic sites and/or determination of allelic patterns of an individual. The genotyping may be performed using a micro-array or a multi-well plate in for instance a laboratory or hospital. It may thus involve the use of a gene/DNA chip or a strip or solid surface comprising one or more nucleic acid molecules.

A further aspect of the invention pertains to a method for diagnosing an individual as being likely to succeed in a dietary weight loss intervention program, the method comprising the steps of genotyping nucleic acid of the individual for at least one SNP selected from the group of SNP markers as set forth in Tables 1 to 4. Preferably, the SNP marker is rs183766 (SEQ ID NO:17). Furthermore, the invention is directed to a method for diagnosing an individual as being likely to succeed in a dietary weight loss intervention program, wherein the individual is treated with a specific type of diet e.g. a low fat/high carbohydrate diet or a high fat/low carbohydrate diet, the method comprising the steps of genotyping nucleic acid of the individual for at least one SNP selected from the group of SNP markers as set forth in Tables 2 and 3. The individual can be diagnosed as being likely to succeed in a dietary weight loss intervention program, wherein the individual is treated with a low fat/high carbohydrate diet, if at least one SNP marker as set forth in Table 2 is detected. Alternatively, the individual can also be diagnosed as being likely to succeed in a dietary weight loss intervention program, wherein the individual is treated with a high fat/low carbohydrate diet, if at least one SNP marker as set forth in Table 3 is detected. The SNPs that are associated with successful completion of certain types of diet can be assessed from the results shown herein.

The invention also pertains to a method of assessing the desirability of treating an individual with a specific type of diet e.g. a hypo-caloric diet, a low fat/high carbohydrate diet or a high fat/low carbohydrate diet. In a further aspect the present invention provides a method of assessing the advisability that the individual should employ a dietary weight loss intervention program comprising a specific type of diet e.g. a hypo-caloric diet, a high fat/low carbohydrate diet or low fat/high carbohydrate diet. The invention further provides a method of assessing the desirability of supplementing the food of the individual with a specific type of diet e.g. a hypo-caloric diet, a high fat/low carbohydrate diet or low fat/high carbohydrate diet. The invention is also directed to a method of determining whether an individual is a suitable candidate for a dietary weight loss intervention program comprising a specific type of diet e.g. a hypo-caloric diet, a high fat/low carbohydrate diet or low fat/high carbohydrate diet. In another aspect the invention relates to a method of assessing the advisability that an individual should employ a specific type of diet e.g. a hypo-caloric diet, a high fat/low carbohydrate diet or low fat/high carbohydrate diet. The above methods can be performed by identifying markers in nucleic acid of the individual that are indicative of an increased predisposition of the individual to successfully complete a dietary weight loss intervention program comprising a specific type of diet. The markers can for instance be selected from the group of markers as shown in Tables 1 to 4. Preferably, the SNP marker is rs183766. The above methods thus determine whether an individual such as an overweight or obese individual is or is not a suitable candidate for a weight management program comprising a specific dietary component. In the methods of the invention the high fat/low carbohydrate or low fat/high carbohydrate diet may be a hypo-energetic diet.

Another aspect of the invention is directed to a kit for use in a method or use of the present invention. The invention thus also provides kits to determine whether an individual is resistant to weight loss based on analysis of genetic polymorphisms. The information can be used to screen individuals, e.g. overweight or obese individuals, and classify them based on their genetic tendency to lose weight or be resistant to lose weight. The polymorphisms as found herein are useful in predicting the outcome of bodyweight management strategies, particularly strategies having a component of dietary intervention either alone or as their main component. The kit comprises at least one primer or primer pair suitable for determining (or being associated with) the likelihood that an individual such as an overweight or obese individual successfully completes or unsuccessfully completes a dietary weight loss intervention program, more in particular a dietary component thereof such as a diet. In an embodiment the invention is directed to a kit for use in a method or use according to the invention, the kit comprising at least one primer or primer pair for genotyping a marker in a gene or locus associated with obesity or an obesity-associated phenotype. Preferably, the marker is a SNP as shown in one of the Tables 1 to 4, even more preferred the marker is SNP rs183766 (SEQ ID NO:17). The primers may be suitable for nucleic acid sequence amplification. Often the kits contain one or more primers or primer pairs hybridizing to different forms of a polymorphism, e.g. a primer or primer pair capable of hybridizing to a first allelic form of a SNP (e.g. A allelic form) and a primer or primer pair capable of hybridizing to a second allelic form of the SNP (e.g. T allelic form). Moreover, kits according to the invention may comprise instructions explaining correlation of the genotype to increased likelihood of successful completion of a specific type of diet such as a hypo-caloric diet, high fat/low carbohydrate diet or a low fat/high carbohydrate diet. Furthermore, the kit may comprise instructions explaining that detection of the presence and/or absence of certain SNPs, such as SNPs selected from the group of SNPs as shown in Tables 2 and 3, is indicative of an increased predisposition of the individual to successfully complete a dietary weight loss intervention program comprising a specific type of diet, e.g. a low fat/high carbohydrate diet or a high fat/low carbohydrate diet. On the basis of the results obtained with the kit it can be detected if an individual has an increased likelihood to successfully complete a dietary weight loss intervention program comprising a high fat/low carbohydrate diet compared to a dietary weight loss intervention program comprising a low fat/high carbohydrate diet and vice versa.

Optional additional components of the kit include, for example, restriction enzymes, reverse transcriptase or polymerase, a positive control, a negative control, at least a further primer pair suitable for detecting (other) markers, appropriate buffers for reverse transcription, PCR and/or hybridization reactions, means used to label and nucleotide mix for the PCR reaction. The kits of the invention may thus also comprise one or more primers, primer pairs, probes and/or oligonucleotides suitable for detecting markers such as SNPs which is/are in linkage disequilibrium with a SNP as shown in one of the Tables 1 to 4. It may also contain one or more primers, primer pairs, probes and/or oligonucleotides suitable for detecting another SNP as shown in one of the Tables 1 to 4, preferably rs7230740 (SEQ ID NO:29), rs751539 (SEQ ID NO:27), rs8047814 (SEQ ID NO:32), rs2084635 (SEQ ID NO:33), rs655970 (SEQ ID NO:3), rs621750 (SEQ ID NO:2), rs11073977 (SEQ ID NO:34), rs11679740 (SEQ ID NO:11), rs3755264 (SEQ ID NO:5), rs3771138 (SEQ ID NO:4), rs203142 (SEQ ID NO:18), rs6751402 (SEQ ID NO:13), rs6958502 (SEQ ID NO:19), rs505922 (SEQ ID NO:25), rs2247380 (SEQ ID NO:12), rs7230740 (SEQ ID NO:29), rs12466364 (SEQ ID NO:10), rs963103 (SEQ ID NO:30), rs6442037 (SEQ ID NO:15), rs1019019 (SEQ ID NO:21), rs928571 (SEQ ID NO:35), rs247979 (SEQ ID NO:36), rs11719455 (SEQ ID NO:16), rs4890647 (SEQ ID NO:37), rs657152 (SEQ ID NO:24), rs928534 (SEQ ID NO:38), rs11058150 (SEQ ID NO:39), rs3779341 (SEQ ID NO:20), rs11684785 (SEQ ID NO:40), rs6750788 (SEQ ID NO:41), rs1471910 (SEQ ID NO:42), rs6434276 (SEQ ID NO:43), rs7831030 (SEQ ID NO:22), rs10899257 (SEQ ID NO:44), rs876614 (SEQ ID NO:6), rs920965 (SEQ ID NO:23), rs2043448 (SEQ ID NO:45), rs1515241 (SEQ ID NO:46), rs12619445 (SEQ ID NO:47), rs2130858 (SEQ ID NO:48), rs6718009 (SEQ ID NO:49), rs4492387 (SEQ ID NO:50), rs11733026 (SEQ ID NO:51), rs1109425 (SEQ ID NO:52), rs3753472 (SEQ ID NO:53), rs7355583 (SEQ ID NO:54), rs7700981 (SEQ ID NO:55), rs10140366 (SEQ ID NO:56), rs13220420 (SEQ ID NO:57), rs6010669 (SEQ ID NO:58), rs2428514 (SEQ ID NO:59), rs3731612 (SEQ ID NO:60), rs10818023 (SEQ ID NO:61), rs4691707 (SEQ ID NO:62), rs17407330 (SEQ ID NO:63), rs1507081 (SEQ ID NO:64), rs12828607 (SEQ ID NO:65), rs12511535 (SEQ ID NO:66) and any combination thereof.

In addition, a kit according to the present invention may contain instructions for carrying out the methods as well as a listing of the obesity-associated alleles and haplotypes relevant in view of the present invention. The components of the kit may be either in dry form in a tube or a vial or dissolved in an appropriate buffer.

The present invention employs, unless otherwise indicated, conventional (recombinant) techniques of molecular biology, immunology, microbiology, biochemistry and cell biology which are well within the skill of a person skilled in the art. All publications and references cited in the present application are incorporated by reference in their entirety for any purpose.

Furthermore, the present invention relates to the use of a low fat/high carbohydrate diet in the manufacture of a medicament for the treatment and/or prevention of obesity in an individual e.g. an overweight or obese individual which has been identified as having at least one SNP that is indicative of an increased likelihood of the individual to successfully complete a dietary weight loss intervention program comprising a low fat/high carbohydrate diet compared to a dietary weight loss intervention program comprising a high fat/low carbohydrate diet. Such SNPs can be found in Table 2.

Furthermore, the present invention relates to the use of a high fat/low carbohydrate diet in the manufacture of a medicament for the treatment and/or prevention of obesity in an individual e.g. an overweight or obese individual which has been identified as having at least one SNP that is indicative of an increased likelihood of the individual to successfully complete a dietary weight loss intervention program comprising a high fat/low carbohydrate diet compared to a dietary weight loss intervention program comprising a low fat/high carbohydrate diet. Such SNPs can be found in Table 3.

Moreover, the present invention relates to the use of a hypo-caloric diet in the manufacture of a medicament for the treatment and/or prevention of obesity in an individual e.g. an overweight or obese individual which has been identified as having at least one SNP as presented in Table 1. In a preferred embodiment said SNP is rs183766.

In addition, the present invention relates to the use of a hypo-caloric diet in the manufacture of a medicament for the treatment and/or prevention of obesity in a female individual e.g. an overweight or obese female individual which has been identified as having at least one SNP as presented in Table 4. In a preferred embodiment said SNP is rs183766.

The present invention also relates to computer systems and computer readable media for storing data according to the present invention. Computer readable media mean media that can be read and accessed directly by a computer including but not being limited to magnetic storage media e.g. floppy discs, hard disc storage media and magnetic tapes; optical storage media e.g. CD-ROM; electrical storage media e.g. RAM and ROM; and hybrids of these categories e.g. magnetic/optical storage media. The data can be stored in one or more databases and include information relating to markers e.g. SNPs as shown in one of the Tables 1 to 4 suitable for determining the likelihood that an individual successfully or unsuccessfully completes a specific dietary weight loss intervention program. The databases may further include information regarding the nature of the marker (e.g. the base occupying a polymorphic position in a reference allele as well as in a non-reference allele), the location of the marker (e.g. by reference to for example a chromosome or distance to known markers within the chromosome), the level of association of the marker with obesity, the frequency of the marker in the population or a subpopulation, the association of the marker with other markers as well as all relevant information about the other markers. It may also include sequences of 10-100 contiguous bases, or their complements, comprising a polymorphic position. The databases may also contain personal information of individuals originating from interviews, questionnaires or surveys as well as relevant medical information originating from doctors, physicians, dieticians, nutritionists or genetic counselors. In addition, the databases may comprise information regarding all types of diets, dietary components and dietary weight loss intervention programs (including composition, price, dosage, etc). It may even comprise information regarding which diet, dietary component and dietary weight loss intervention program is suitable and/or not suitable for an individual on the basis of its genetic profile. The databases may comprise information from one individual but also from a group of individuals (e.g. a specific population or subpopulation). The databases may be used in the methods and uses of the present invention. Typically, genetic data from an individual will be introduced into the computer system by means of electronic means, for example by using a computer. Next, the genetic data are compared to the data in the databases comprising information relating to genetic markers. On the basis of the comparison the likelihood of an individual to successfully complete a dietary weight loss intervention program can be determined and, optionally, a suitable personalized diet can be advised. The invention also provides a computer program comprising program code means for performing all the above steps when said program is run on a computer. Also provided is a computer program product comprising program code means stored on a computer readable medium for performing the methods and uses of the invention when said program is run on a computer. A computer program product comprising program code means on a carrier wave that, when executed on a computer system, instruct the computer system to perform the above steps is additionally provided. Moreover, the invention provides an apparatus arranged to perform the above steps. The apparatus typically comprises a computer system, such as a PC. In one embodiment, the computer system comprises means for receiving genetic data from an individual, a module for comparing the data with a database comprising information relating to genetic markers, and means for determining on the basis of said comparison the likelihood that an individual will successfully complete or fail to successfully complete a dietary weight loss intervention program and optionally even means to determine a suitable diet, dietary component or dietary weight loss intervention program for an individual. Access to the databases can be accomplished electronically, e.g. via a computer (PC or laptop), mobile phone, personal digital assistance, internet, handheld but the information in the databases can also be provided in paper form. People having access to the databases may be the individuals themselves, physicians, nutritionists, doctors, dieticians, and even restaurants and supermarkets. Access may be complete or limited to certain data only. The above systems, media, programs and apparatuses may also comprise an algorithm to calculate the benefit probability using the genetic input in addition to phenotypic data such as e.g. starting weight, ethnicity, date of birth, sex.

EXAMPLES

To illustrate the invention, the following examples are provided. These examples are not intended to limit the scope of the invention.

Example 1 Aim

A 10-week dietary weight loss intervention study was performed to examine the interaction between genetic factors and obesity related phenotypes. In order to achieve this goal, a whole genome association study was performed to identify genes associated with quantitative traits involved in weight loss/gain and in respect to co-variables of nutrient intake or more generally in respect to diet. Thus 318237 SNPs have been genotyped on the 771 obese individuals with the Illumina HumanHap 300-DUO SNP Chip.

Description of the Cohort

In a 10-week, European, multi-center dietary intervention study 771 weight stable, obese (BMI>=30 kg/m2), but otherwise healthy men and women were randomized to a low fat/high carbohydrate (20-25% energy from fat; 60-65% from carbohydrate) or high fat/low carbohydrate (40-45% energy from fat; 40-45% from carbohydrate), hypo-energetic diet (energy deficiency of 600 kcal/day).

Selection of Patients

Obese subjects were recruited from May 2001 until September 2002. Inclusion criteria were: BMI>=30 and age 20-50. Exclusion criteria were: weight change >3 kg within the last 3 months prior to study start, hypertension, diabetes or hyperlipidemia treated by drugs, untreated thyroid disease, surgically or drug-treated obesity, pregnancy, and participation in other trials, and alcohol or drug abuse. Informed written consent was obtained prior to study participation and the study was approved by the Ethical Committee at each of the participating centers. The study has been described in detail elsewhere (see Petersen et al. (2006) and Sørensen et al. (2006)).

Analysis of Phenotypes

Body weights were measured on calibrated scales. Waist circumferences were measured with the participant wearing only non-restrictive underwear. Body height was measured with a calibrated stadiometer. The mean of three measurements was recorded for each variable. Fat mass and fat-free mass were assessed by multifrequency bio-impedance (Bodystat; QuadScan 4000, Isle of Man, British Isles). Resting metabolic rate was measured by ventilated hood systems routinely used at each centre and a standardized validation program was used to facilitate pooling of the results from the different centres. Venous blood samples were drawn after an overnight fast of 12 hours, following a 3-day period when subjects had been instructed to avoid excessive physical activity or alcohol consumption. Subjects rested in the supine position for 15 minutes prior to the procedure. Insulin secretion and insulin resistance were measured by HOMA.Statistical modelling. Separate linear regression models were made for effect on weight loss, change in fasting glucose, change in fasting insulin, change in insulin secretion and change in insulin resistance.

Covariates

Statistical analyses were adjusted for baseline weight at the beginning of the intervention (as described in Sørensen et al. (2006)), gender, age, center and diet group.

Preparation of Samples

High molecular weight genomic and mitochondrial DNA was isolated from blood samples using routine methods. Concentration of purified DNA in each sample was measured using Syber Green II quantification method. For genotyping using the Illumina platform a minimum of 750 ng with a concentration of 50 ng/ul of genomic DNA is necessary, therefore each DNA sample was diluted accordingly. Of the 771 samples from obese individuals, 751 met these criteria.

Genome-Wide Scanning Using Illumina HumanHap 300-DUO Chips

The whole genome genotyping of the DNA samples was performed using the Illumina HumanHap 300-DUO SNP BeadChips and Infinium II genotyping assay. The HumanHap 300-DUO BeadChips contains over 317000 SNP markers of which majority are tagSNP markers derived from the International HapMap Project. TagSNPs are loci that can serve as proxies for many other SNPs. The use of tagSNPs greatly improves the power of association studies as only a subset of loci needs to be genotyped while maintaining the same information and power as if one had genotyped a larger number of SNPs.

The Infinium II genotyping with the HumanHap300DUO BeadChips were performed according to the “Single-Sample BeadChip Manual process” described in detail in “Infinium™ II Assay System Manual” provided by Illumina (San Diego, Calif., USA). Briefly, 750 ng of genomic DNA from a sample was subjected to whole genome amplification. The amplified DNA was fragmented, precipitated and resuspended in hybridization buffer. The resuspended sample was heat denatured and then applied to one HumanHap300DUO BeadChip. After overnight hybridization, mis- and non-hybridized DNA was washed away from the BeadChip and allele-specific single-base extension of the oligonucleotides on the BeadChip was performed, using labelled deoxynucleotides and the captured DNA as a template. After staining of the extended DNA, the BeadChips were washed and scanned with the BeadArray Reader (Illumina) and genotypes from samples were called by using the BeadStudio software (Illumina). All 751 DNA samples that met the quality requirements of the Illumina platform were genotyped.

Statistical Analysis of the GWS Data of the Nugenob Study:

Firstly, quality control measures were done. All genotyped SNPs were tested for Hardy Weinberg equilibrium using a package R:genetics. SNPs which showed a deviation from the Hardy Weinberg equilibrium were flagged to investigate any possible genotyping errors.

To study the population structure a random set of 27974 SNPs covering all autosomal chromosomes were selected and then analysed using the plink software that uses complete linkage agglomerative clustering, based on pair wise identity-by-state distance. In addition, 602 ancestry informative markers for European populations were selected to detect population stratification using plink and STRUCTURE software. The conclusion of all analyses was that there was no significant population stratification in this study cohort.

Call frequencies (number of delivered genotypes per SNP) for more than 99% of the SNPs were >=98%. Call rates (number of genotyped SNPs per individual) for more than 98% of the individuals were >=95%, both consistent with the specifications of the manufacturer, indicating an accurate and reliable genotyping.

Then, statistical analysis was done. Concerning the association between weight loss and genetic component, multiple linear regression analysis using HelixTree (GoldenHelix Inc.) using gender, age, center, diet group and weight at the beginning of the intervention as covariates and several models (additive, dominant and recessive) well known to the person skilled in the art using a multiple linear regression with R statistical software controlling for gender, age, center, diet group, and weight at the beginning of the intervention, with and without controlling for an interaction between diet and genetic component, as described in e.g. Sørensen et al. (2006) were applied.

Results of the Whole Genome Association Study (WGAS):

Genome wide scanning—whole genome association analysis in weight loss using European, multi-center dietary intervention study subjects and Illumina HumanHap-300DUO BeadChips was carried out. The final data set used in the statistical analysis included 750 subjects. In this study new weight loss associated SNP markers, of which several are intragenic, were found. The results of various statistical analyses of the WGAS are presented in the following tables:

Table 1: SNP markers associated with weight loss (without diet—gene interaction) found using the additive, recessive or dominant association model or multiple linear regression analysis; all individuals in the present study were subjected to a hypo-caloric diet, SNPs found in individuals that finished the intervention and that showed weight loss (i.e. having successfully completed the program) are presented in Table 1.

Table 2: SNP markers associated with weight loss (with low fat/high carbohydrate diet—gene interaction) found using the additive, recessive or dominant association model or multiple linear regression analysis; a group of individuals in the present study were subjected to a hypo-caloric, low fat/high carbohydrate diet, SNPs found in individuals of that group that finished the intervention and that showed weight loss (i.e. having successfully completed the program) are presented in Table 2.

Table 3: SNP markers associated with weight loss (with high fat/low carbohydrate diet—gene interaction) found using the additive, recessive or dominant association model or multiple linear regression analysis; a group of individuals in the present study were subjected to a hypo-caloric, high fat/low carbohydrate diet, SNPs found in individuals of that group that finished the intervention and that showed weight loss (i.e. having successfully completed the program) are presented in Table 3.

Table 4: SNP markers associated with weight loss in females (without diet—gene interaction) found using the additive, recessive or dominant association model or multiple linear regression analysis; all female individuals in the present study were subjected to a hypo-caloric diet, SNPs found in female individuals that finished the intervention and that showed weight loss (i.e. having successfully completed the program) are presented in Table 4.

REFERENCES

  • Petersen M, Taylor M A, Saris W H M, Verdich C, Toubro S, Macdonald I, Rossner S, Stich V, Guy-Brand B, Langin D, Martinez J A, Pedersen O, Holst C, Sørensen T I A, Astrup A and The Nugenob Consortium (2006), Randomized, multi-center trial of two hypo-energetic diets in obese subjects: high- versus low-fat content. Int. J. Obes. (Lond.). 30:552-60.
  • Sørensen T I A, Boutin P, Tayloer M A, Larsen L H, Verdich C, Petersen L, Hoist C, Echwald S M, Dina C, Tourbo S, Petersen M, Polak J, Clement K, Martinez J A, Langin D, Oppert J_M, Stch V, Macdonald I, Amer P, Saris W H M, Pedersen O, Astrup A, Froguel P and The Nugenob Consortium (2006), Genetic polymorphisms and weight loss in obesity: a randomised trial of hypo-energetic high- versus low-fat diets. PLoS Clinical Trials 1(2):e12.

TABLE 1 SNPs that are associated with weight loss without considering a diet-gene interaction. SNP Name Chromosome Position Analysis P-Value rs1515241 2 146457262 additive 3.98866E−07 rs3755264 2 10463686 additive 1.89522E−06 rs183766 5 75446386 additive 1.90897E−06 rs3771138 2 10462090 additive 2.04854E−06 rs3881953 1 200794643 additive 2.08422E−06 rs12619445 2 146108819 additive 2.16444E−06 rs6461337 7 17701436 additive 5.34452E−06 rs12734338 1 200736345 additive 8.93686E−06 rs505922 9 135139049 additive  9.2418E−06 rs920965 8 72383082 additive 1.31439E−05 rs2130858 2 146468630 additive  1.4992E−05 rs7831030 8 72382632 additive 1.62893E−05 rs1858094 9 105442842 additive 1.95036E−05 rs657152 9 135129085 additive 1.96495E−05 rs797351 14 33662442 additive  1.9904E−05 rs835750 11 44842774 additive 2.00928E−05 rs12743401 1 200743270 additive 2.52222E−05 rs6718009 2 146491969 additive 2.66267E−05 rs6751402 2 231074805 additive 2.68453E−05 rs9561023 13 91820401 additive  2.823E−05 rs1156672 2 56624351 additive 2.83493E−05 rs4492387 8 123478740 additive 3.30656E−05 rs963103 21 25950827 additive 3.41826E−05 rs1567989 2 146397787 additive 3.59374E−05 rs247979 3 174635452 additive 3.84523E−05 rs131349 22 22173674 additive 4.77159E−05 rs12623125 2 146385651 additive 4.89748E−05 rs3790033 13 50469545 additive 5.06131E−05 rs6958502 7 5294954 additive 5.06581E−05 rs140156 22 22165038 additive 5.47138E−05 rs10773456 12 127015004 additive 5.47359E−05 rs1978531 2 56597170 additive 5.51949E−05 rs281562 18 63414752 additive 5.52869E−05 rs4034627 12 126963424 additive 5.61683E−05 rs11640875 16 81278924 additive 5.71441E−05 rs203142 6 138698204 additive 5.87123E−05 rs876614 2 10466027 additive  6.2333E−05 rs9341731 6 63917246 additive 6.29007E−05 rs655970 1 239821458 additive 6.33513E−05 rs6430423 2 133937867 additive 6.95873E−05 rs1109425 2 146561504 additive 7.40051E−05 rs299216 5 118424126 additive 7.63175E−05 rs1477521 2 56607982 additive 7.79162E−05 rs11968591 6 130157919 additive  7.9242E−05 rs1909468 9 105495349 additive 8.33809E−05 rs11719455 3 196381720 additive 8.36603E−05 rs13389057 2 222645235 additive 8.42552E−05 rs13390990 2 133945386 additive 9.23314E−05 rs4700347 5 59286262 additive 9.40013E−05 rs3786004 17 46428529 additive 9.58622E−05 rs7237084 18 52746015 additive 9.67628E−05 rs4768048 12 4255138 dominant 6.67489E−07 rs1515241 2 146457262 dominant 1.47544E−06 rs3881953 1 200794643 dominant 2.08422E−06 rs203142 6 138698204 dominant 2.65184E−06 rs6461337 7 17701436 dominant 4.51434E−06 rs9561023 13 91820401 dominant 8.83336E−06 rs12734338 1 200736345 dominant 8.93686E−06 rs281562 18 63414752 dominant 9.04696E−06 rs797351 14 33662442 dominant 9.26973E−06 rs12619445 2 146108819 dominant 9.30395E−06 rs11640875 16 81278924 dominant 1.21104E−05 rs2863991 1 107350267 dominant 1.23616E−05 rs3771138 2 10462090 dominant 1.34551E−05 rs9341731 6 63917246 dominant 1.35485E−05 rs6751402 2 231074805 dominant 1.39548E−05 rs3755264 2 10463686 dominant 1.61137E−05 rs12570348 10 126428952 dominant 2.07123E−05 rs6430423 2 133937867 dominant 2.18248E−05 rs5963085 X 38577448 dominant 2.43729E−05 rs7582028 2 56492359 dominant 2.44241E−05 rs12743401 1 200743270 dominant 2.52222E−05 rs835750 11 44842774 dominant 2.78044E−05 rs183766 5 75446386 dominant 2.82185E−05 rs4034627 12 126963424 dominant 3.17746E−05 rs13390990 2 133945386 dominant 3.25136E−05 rs2642219 X 96026061 dominant 3.38753E−05 rs135557 22 44919890 dominant 3.44035E−05 rs1488144 12 126958312 dominant 4.84039E−05 rs38092 7 78066827 dominant 5.02077E−05 rs3786004 17 46428529 dominant 5.19898E−05 rs2130858 2 146468630 dominant 5.38921E−05 rs1858094 9 105442842 dominant 5.41793E−05 rs10490096 2 58923432 dominant 5.52332E−05 rs1156672 2 56624351 dominant 5.74615E−05 rs1603708 8 55961289 dominant 6.68392E−05 rs7531902 1 95607711 dominant 6.77026E−05 rs746810 6 96596809 dominant 7.38833E−05 rs3852738 16 81271051 dominant  7.7046E−05 rs10284107 X 142118310 dominant 7.76714E−05 rs645184 11 73806780 dominant 8.10062E−05 rs7883690 X 118034043 dominant 8.67859E−05 rs233218 X 95990022 dominant 8.74239E−05 rs505922 9 135139049 dominant 8.78012E−05 rs233206 X 96002756 dominant 8.90192E−05 rs1952151 14 20610702 dominant 8.92472E−05 rs5920027 X 145048449 dominant 9.00374E−05 rs7876773 X 69116783 dominant 9.02249E−05 rs4962144 9 135264961 dominant 9.29135E−05 rs6718009 2 146491969 dominant 9.31294E−05 rs10773456 12 127015004 dominant 9.39181E−05 rs2792232 9 6965622 dominant 9.43553E−05 rs4430266 X 95822496 dominant 9.49721E−05 rs3779341 7 77506050 dominant  9.8783E−05 rs3898332 X 67094527 recessive 1.85868E−06 rs10521379 X 86554148 recessive 2.89023E−06 rs963103 21 25950827 recessive 4.51096E−06 rs6778912 3 117850884 recessive 7.84318E−06 rs2473206 X 98123682 recessive 1.03479E−05 rs5921261 X 98100962 recessive 1.15142E−05 rs4492387 8 123478740 recessive 2.00452E−05 rs920965 8 72383082 recessive 2.12102E−05 rs11095531 X 10243238 recessive 2.27192E−05 rs5918801 X 67125659 recessive 2.45899E−05 rs5982533 X 111533119 recessive 2.60199E−05 rs5918809 X 67204577 recessive 2.79859E−05 rs11635733 15 68320485 recessive 2.80642E−05 rs1325719 X 97546599 recessive 3.29341E−05 rs5953883 X 142696457 recessive 3.29421E−05 rs6615988 X 97530968 recessive 3.34218E−05 rs5759855 22 22179043 recessive 3.39592E−05 rs17098767 14 31875424 recessive 3.73097E−05 rs7032088 9 77039544 recessive 3.93374E−05 rs7831030 8 72382632 recessive 3.94713E−05 rs17222873 X 111681992 recessive 4.11355E−05 rs1917416 2 34825217 recessive 4.20742E−05 rs2191081 5 128053706 recessive 4.40023E−05 rs9527966 13 58786086 recessive  4.6873E−05 rs515495 11 82100987 recessive 5.13234E−05 rs1019135 5 128060202 recessive 5.27749E−05 rs2280925 X 152511013 recessive 5.77672E−05 rs12688347 X 38015430 recessive 6.33689E−05 rs6616047 X 98065036 recessive 6.34774E−05 rs1279817 X 123220609 recessive 6.35038E−05 rs4456006 X 66944946 recessive 6.42336E−05 rs3788855 X 67198192 recessive 6.57972E−05 rs12285441 11 86463457 recessive  6.6335E−05 rs7062976 X 146229118 recessive 6.87374E−05 rs717680 19 36713838 recessive  6.9771E−05 rs739668 X 8833606 recessive 7.20197E−05 rs8184900 21 42102167 recessive 7.26306E−05 rs5934819 X 10175416 recessive 7.29481E−05 rs5764280 22 42494725 recessive 7.47618E−05 rs7062732 X 7608976 recessive 8.49756E−05 rs5920712 X 98086280 recessive 8.51908E−05 rs5917678 X 38749913 recessive 8.67948E−05 rs6029424 20 38962451 recessive 8.95457E−05 rs10494266 1 148512175 recessive 9.05305E−05 rs4827556 X 66979214 recessive 9.20308E−05 rs4749114 10 26670035 recessive 9.30319E−05 rs5917677 X 38749573 recessive 9.57023E−05 rs5910122 X 123965796 recessive 9.77284E−05 rs1515241 2 146457262 Regression 6.34124E−07 rs11679740 2 204023638 Regression 2.70583E−06 rs12619445 2 146108819 Regression 3.83718E−06 rs183766 5 75446386 Regression 4.45986E−06 rs3755264 2 10463686 Regression 6.62627E−06 rs3771138 2 10462090 Regression 7.97181E−06 rs2130858 2 146468630 Regression 1.07234E−05 rs6751402 2 231074805 Regression 1.27894E−05 rs9561023 13 91820401 Regression 1.44494E−05 rs4492387 8 123478740 Regression 1.61377E−05 rs11733026 4 65581964 Regression 2.40062E−05 rs10284107 X 142118310 Regression 2.45777E−05 rs6772835 3 176545888 Regression 2.48583E−05 rs2247380 2 204072965 Regression 2.75264E−05 rs505922 9 135139049 Regression 2.78535E−05 rs1858094 9 105442842 Regression 2.88232E−05 rs7230740 18 3626287 Regression 2.90304E−05 rs11600358 11 130366453 Regression 2.92195E−05 rs6718009 2 146491969 Regression 2.93914E−05 rs203142 6 138698204 Regression 3.11776E−05 rs203217 4 108540333 Regression 3.20622E−05 rs6958502 7 5294954 Regression 3.83614E−05 rs1567989 2 146397787 Regression 3.88486E−05 rs12466364 2 203934575 Regression 3.94679E−05 rs1917416 2 34825217 Regression 4.13527E−05 rs6442037 3 46904549 Regression 4.15405E−05 rs38092 7 78066827 Regression 4.39373E−05 rs963103 21 25950827 Regression 4.53915E−05 rs3779341 7 77506050 Regression 4.92935E−05 rs4768048 12 4255138 Regression 4.94769E−05 rs12623125 2 146385651 Regression 5.02819E−05 rs1455023 5 67443595 Regression 5.04186E−05 rs7831030 8 72382632 Regression  5.1941E−05 rs657152 9 135129085 Regression 5.24942E−05 rs4700347 5 59286262 Regression 5.32484E−05 rs2863991 1 107350267 Regression 5.94297E−05 rs11719455 3 196381720 Regression 5.99784E−05 rs9840169 3 152204089 Regression 6.02151E−05 rs920965 8 72383082 Regression  6.1033E−05 rs923741 8 24347622 Regression 6.18961E−05 rs952172 2 204150877 Regression 6.32684E−05 rs247979 3 174635452 Regression 6.52671E−05 rs3790033 13 50469545 Regression 6.74589E−05 rs10428822 6 166523754 Regression 7.26257E−05 rs1019019 7 77517590 Regression 7.51547E−05 rs6430423 2 133937867 Regression 7.56077E−05 rs9363095 6 94369998 Regression  7.7987E−05 rs930191 2 178760197 Regression 7.86836E−05 rs17041200 12 94541567 Regression 8.68396E−05 rs876792 12 13321482 Regression  8.7535E−05 rs2110037 1 185999482 Regression 8.77107E−05 rs7192220 16 60904727 Regression 8.82878E−05 rs1109425 2 146561504 Regression 9.04843E−05 rs4466603 1 185974917 Regression 9.39125E−05 rs7032088 9 77039544 Regression 9.47418E−05 rs11117760 1 215288740 Regression 9.59176E−05 rs13390990 2 133945386 Regression  9.5934E−05 rs6713259 2 42294253 Regression 9.59975E−05 rs203738 4 108475811 Regression 9.82485E−05 rs183766 5 75446386 additive   2.00E−06 rs1515241 2 146457262 additive   3.00E−06 rs11679740 2 204023638 additive   4.00E−06 rs3755264 2 10463686 additive   7.00E−06 rs3771138 2 10462090 additive   8.00E−06 rs12619445 2 146108819 additive   1.00E−05 rs9653448 2 104979770 additive   2.00E−05 rs203142 6 138698204 additive   2.00E−05 rs224185 16 3143035 additive   2.00E−05 rs2130858 2 146468630 additive   2.50E−05 rs6718009 2 146491969 additive   2.60E−05 rs751539 13 50531726 additive   2.60E−05 rs621750 1 84493995 additive   2.70E−05 rs4492387 8 123478740 additive   2.80E−05 rs11733026 4 65581964 additive   3.20E−05 rs6751402 2 231074805 additive   3.90E−05 rs6958502 7 5294954 additive   4.50E−05 rs505922 9 135139049 additive   4.50E−05 rs11897499 2 34809902 additive   4.60E−05 rs1109425 2 146561504 additive   4.90E−05 rs606816 1 84492963 additive   5.00E−05 rs17041200 12 94541567 additive   5.70E−05 rs2247380 2 204072965 additive   6.10E−05 rs1858094 9 105442842 additive   6.50E−05 rs7230740 18 3626287 additive   7.40E−05 rs12466364 2 203934575 additive   8.50E−05 rs6442037 3 46904549 additive   9.40E−05 rs963103 21 25950827 additive   9.40E−05 rs10990823 9 105414775 additive   9.50E−05 rs11073977 15 89415721 additive   9.50E−05 rs1019019 7 77517590 additive   9.80E−05 rs1515241 2 146457262 dominant   4.09E−06 rs4492387 8 123478740 dominant   6.90E−06 rs963103 21 25950827 dominant   8.46E−06 rs2130858 2 146468630 dominant   2.79E−05 rs1109425 2 146561504 dominant   5.66E−05 rs1019019 7 77517590 dominant   7.78E−05 rs183766 5 75446386 dominant   9.06E−05 rs203142 6 138698204 recessive   1.65E−06 rs12619445 2 146108819 recessive   1.97E−05 rs2247380 2 204072965 recessive   2.05E−05 rs751539 13 50531726 recessive   2.08E−05 rs6751402 2 231074805 recessive   3.01E−05 rs6718009 2 146491969 recessive   3.77E−05 rs11679740 2 204023638 recessive   5.11E−05 rs3771138 2 10462090 recessive   5.41E−05 rs3755264 2 10463686 recessive   5.53E−05 rs10990823 9 105414775 recessive   5.60E−05 rs621750 1 84493995 recessive   6.46E−05 rs224185 16 3143035 recessive   8.39E−05 rs6442037 3 46904549 recessive   8.62E−05 rs183766 5 75446386 recessive   9.40E−05

TABLE 2 SNP markers associated with weight loss considering a low fat/high carbohydrate diet - gene interaction. SNP Name Chromosome Position Analysis P-Value rs1533015 5 33031359 additive  6.7989E−06 rs12511535 4 45177877 additive 9.35815E−06 rs334132 2 178751905 additive 9.53909E−06 rs963103 21 25950827 additive 1.21504E−05 rs6615988 X 97530968 additive 1.22547E−05 rs1498179 8 55951422 additive 1.52936E−05 rs13389057 2 222645235 additive 2.30191E−05 rs6883139 5 26218595 additive 2.42965E−05 rs4691707 4 156660763 additive  2.9486E−05 rs10818023 9 99554739 additive 3.44223E−05 rs1325719 X 97546599 additive 3.65306E−05 rs9561023 13 91820401 additive 3.65889E−05 rs586788 18 3518848 additive 3.90701E−05 rs1507081 8 59182821 additive 4.12124E−05 rs17284515 18 67188946 additive 5.48984E−05 rs2507710 8 37608456 additive 5.81056E−05 rs131349 22 22173674 additive 5.81389E−05 rs6461337 7 17701436 additive  6.1074E−05 rs9672039 14 84645568 additive 6.54874E−05 rs17807368 4 181876969 additive 6.67915E−05 rs4944457 11 83414626 additive 7.06078E−05 rs2416744 9 121817831 additive 8.01351E−05 rs876032 1 18340679 additive 8.49248E−05 rs6964189 7 154016705 additive 8.53133E−05 rs1008196 1 30510029 additive 8.78195E−05 rs3731612 2 119459334 additive 9.41192E−05 rs1373838 13 92927596 additive  9.5612E−05 rs4768048 12 42515138 dominant 3.42408E−06 rs586788 18 3518848 dominant 7.91006E−06 rs12511535 4 45177877 dominant 9.11681E−06 rs1498179 8 55951422 dominant 1.05235E−05 rs9561023 13 91820401 dominant 2.16505E−05 rs1785113 18 13473635 dominant 2.29162E−05 rs3007733 1 18673497 dominant 2.40278E−05 rs4780896 16 10226031 dominant 2.59283E−05 rs285545 2 4704105 dominant 2.85608E−05 rs10818023 9 99554739 dominant 2.90475E−05 rs2992753 1 18680878 dominant 2.95297E−05 rs11660123 18 48159350 dominant 3.85762E−05 rs131349 22 22173674 dominant 3.95722E−05 rs1507081 8 59182821 dominant 4.12124E−05 rs13389057 2 222645235 dominant 4.28134E−05 rs1533015 5 33031359 dominant 4.63179E−05 rs237168 16 26564453 dominant 5.39229E−05 rs17700671 2 76193287 dominant 5.45803E−05 rs17513917 1 91977784 dominant 6.18469E−05 rs2142259 21 17319547 dominant 6.22833E−05 rs1916870 2 76284448 dominant 6.70633E−05 rs1787454 18 52772680 dominant 7.14845E−05 rs6772835 3 176545888 dominant 7.56866E−05 rs140156 22 22165038 dominant 7.89347E−05 rs9672039 14 84645568 dominant 8.02417E−05 rs6597439 7 154034944 dominant 8.14664E−05 rs876032 1 18340679 dominant  8.5497E−05 rs1321623 1 34079291 dominant 8.82529E−05 rs6751402 2 231074805 dominant 8.87591E−05 rs4813974 20 11190432 dominant 8.99289E−05 rs10825973 10 58472427 dominant 9.22963E−05 rs4534106 8 108858991 dominant 9.44731E−05 rs937725 2 98360845 dominant 9.48202E−05 rs1471132 12 621568 dominant 9.73114E−05 rs11884795 2 133235973 dominant 9.85263E−05 rs1862385 5 158270974 recessive 4.83067E−06 rs4077930 3 120348659 recessive 8.36329E−06 rs1978706 5 158206648 recessive 1.31175E−05 rs6883139 5 26218595 recessive 2.02197E−05 rs6615988 X 97530968 recessive 2.09509E−05 rs1325719 X 97546599 recessive 2.74212E−05 rs6415296 7 10348573 recessive 3.69289E−05 rs9962888 18 69210863 recessive 4.00095E−05 rs7752070 6 15712897 recessive 5.54737E−05 rs6728104 2 224096724 recessive  5.6266E−05 rs9296985 6 15697993 recessive 6.05263E−05 rs7770921 6 15713092 recessive 6.23418E−05 rs10961296 9 13894859 recessive  6.4125E−05 rs7873512 9 104923656 recessive 6.50406E−05 rs872665 9 112301303 recessive 6.61693E−05 rs1474605 6 15766190 recessive 6.72095E−05 rs2042743 18 13417799 recessive 6.89777E−05 rs12362164 11 93415788 recessive 7.70772E−05 rs1397870 11 29360046 recessive 7.77637E−05 rs1562083 5 179558235 recessive 7.80891E−05 rs10521379 X 86554148 recessive 7.88546E−05 rs12057961 1 83918928 recessive 8.42387E−05 rs334132 2 178751905 recessive 8.51862E−05 rs6029424 20 38962451 recessive 8.57888E−05 rs1937701 10 53278982 recessive 9.55487E−05 rs309499 1 23107532 recessive 9.70446E−05 rs4691707 4 156660763 Regression 3.87184E−06 rs6883139 5 26218595 Regression 1.15875E−05 rs1507081 8 59182821 Regression  1.2083E−05 rs9561023 13 91820401 Regression  1.2541E−05 rs963103 21 25950827 Regression 1.53462E−05 rs10818023 9 99554739 Regression 1.64768E−05 rs219472 4 109531089 Regression 1.66544E−05 rs17407330 2 36405926 Regression 1.79938E−05 rs334132 2 178751905 Regression 1.80863E−05 rs4768048 12 42515138 Regression 3.29507E−05 rs1400623 4 156663326 Regression 3.43067E−05 rs12511535 4 45177877 Regression 4.11076E−05 rs1546645 4 156652463 Regression 4.40056E−05 rs1533015 5 33031359 Regression 4.53687E−05 rs2040981 22 30495507 Regression 4.66007E−05 rs928534 6 124183437 Regression 4.96482E−05 rs621750 1 84493995 Regression  5.0512E−05 rs7230740 18 3626287 Regression 5.34335E−05 rs2666900 11 45713976 Regression 5.97498E−05 rs10863310 1 215463633 Regression 6.14927E−05 rs3731612 2 119459334 Regression 6.28653E−05 rs4492387 8 123478740 Regression 6.89488E−05 rs6615988 X 97530968 Regression 7.38782E−05 rs1891550 14 53070046 Regression 7.99011E−05 rs11026033 11 21319339 Regression 8.05971E−05 rs4872381 8 25872292 Regression 8.39166E−05 rs1916870 2 76284448 Regression  8.7414E−05 rs1857373 1 58193422 Regression 8.77239E−05 rs1202762 1 58165352 Regression 8.84492E−05 rs4075894 7 236109 Regression 9.16322E−05 rs586788 18 3518848 Regression 9.47771E−05 rs6964189 7 154016705 Regression 9.84016E−05 rs7665794 4 168477486 Regression 9.84081E−05 rs7926805 11 17715162 Regression  9.9199E−05 rs7279877 21 26090513 Regression 9.95199E−05 rs12828607 12 42945386 Regression 9.96302E−05

TABLE 3 SNP markers associated with weight loss considering a high fat/low carbohydrate diet - gene interaction. SNP Name Chromosome Position Analysis P-Value rs12619445 2 146108819 additive 1.79003E−07 rs179194 5 118969652 additive 3.49369E−06 rs12734338 1 200736345 additive 1.11036E−05 rs2383666 1 185933373 additive 1.26386E−05 rs10496375 2 103868516 additive  1.6028E−05 rs1156672 2 56624351 additive 1.69439E−05 rs7582028 2 56492359 additive 2.03832E−05 rs3881953 1 200794643 additive 2.18358E−05 rs280316 6 50733336 additive 2.23086E−05 rs987237 6 50911008 additive  2.3585E−05 rs2084635 8 82488961 additive 2.67503E−05 rs12743401 1 200743270 additive 2.85899E−05 rs203142 6 138698204 additive 2.93198E−05 rs10773456 12 127015004 additive 3.06937E−05 rs884437 2 28423901 additive 3.22883E−05 rs9298355 8 82468808 additive 4.36394E−05 rs583911 3 161193083 additive 4.46727E−05 rs6954958 7 45249693 additive 4.55438E−05 rs2110037 1 185999482 additive 4.65698E−05 rs7045961 9 91753818 additive 5.34798E−05 rs6486762 12 128081919 additive 5.61576E−05 rs1978531 2 56597170 additive  5.6311E−05 rs871521 2 28440842 additive 5.82515E−05 rs752838 12 127009671 additive 6.35793E−05 rs2256209 1 153046167 additive 7.43157E−05 rs1390190 12 53305923 additive 7.72895E−05 rs3753472 1 201398155 additive 7.94752E−05 rs735890 12 1624684 additive  8.168E−05 rs6718973 2 56610360 additive 8.59392E−05 rs2863991 1 107350267 additive 8.79205E−05 rs6547846 2 28429464 additive 8.85591E−05 rs3857487 6 88020406 additive 9.79703E−05 rs1477521 2 56607982 additive 9.88733E−05 rs12619445 2 146108819 dominant 7.06076E−07 rs2863991 1 107350267 dominant 1.15632E−06 rs203142 6 138698204 dominant 3.68303E−06 rs5909390 X 18058390 dominant 9.59464E−06 rs12734338 1 200736345 dominant 1.11036E−05 rs9298355 8 82468808 dominant 1.59169E−05 rs5955936 X 18046470 dominant 1.83982E−05 rs3881953 1 200794643 dominant 2.18358E−05 rs735890 12 1624684 dominant 2.39999E−05 rs501031 5 4981317 dominant 2.69174E−05 rs12743401 1 200743270 dominant 2.85899E−05 rs2084635 8 82488961 dominant 3.33829E−05 rs132649 22 34883126 dominant 4.04843E−05 rs10496375 2 103868516 dominant 4.12409E−05 rs6954958 7 45249693 dominant 4.28786E−05 rs179194 5 118969652 dominant 4.61246E−05 rs32652 5 118733443 dominant 4.71118E−05 rs233206 X 96002756 dominant 5.01365E−05 rs233218 X 95990022 dominant 5.01365E−05 rs10419232 19 18265147 dominant 5.24249E−05 rs3790033 13 50469545 dominant 6.07679E−05 rs10148795 14 32915072 dominant 6.28612E−05 rs2114442 4 101051082 dominant 7.25451E−05 rs4765710 12 3050473 dominant 7.25452E−05 rs1942989 18 57742924 dominant 7.68045E−05 rs5908353 X 141439007 dominant 8.07656E−05 rs752838 12 127009671 dominant 8.34504E−05 rs4717554 7 69908272 dominant 8.56071E−05 rs4825353 X 18120846 dominant 8.85807E−05 rs5910903 X 116073824 dominant 8.88626E−05 rs10773456 12 127015004 dominant 8.96031E−05 rs583911 3 161193083 dominant  9.3351E−05 rs7045961 9 91753818 dominant 9.43085E−05 rs1488144 12 126958312 dominant 9.66299E−05 rs5925030 X 150681445 dominant 9.76423E−05 rs3857487 6 88020406 dominant 9.79703E−05 rs987237 6 50911008 recessive 6.32204E−07 rs12285441 11 86463457 recessive 7.41655E−07 rs10515135 17 52572721 recessive 1.20864E−06 rs11260507 X 124008638 recessive 1.49254E−06 rs3740660 11 86421389 recessive  3.1195E−06 rs5965630 X 144552861 recessive 3.38021E−06 rs655065 1 49336167 recessive 5.61418E−06 rs6649187 X 123229805 recessive 6.05187E−06 rs590503 1 49353592 recessive 6.32236E−06 rs2188727 X 12120515 recessive 6.96233E−06 rs6608183 X 123224928 recessive 1.09131E−05 rs2227150 X 123984248 recessive 1.36068E−05 rs936656 17 30457932 recessive 1.39301E−05 rs765456 10 100104954 recessive  1.7627E−05 rs1279817 X 123220609 recessive 1.76567E−05 rs5979224 X 9908823 recessive 1.84866E−05 rs2901211 10 121119102 recessive 2.05848E−05 rs2704838 X 24088962 recessive 2.70589E−05 rs10501632 11 86402151 recessive 2.84861E−05 rs5910122 X 123965796 recessive 3.09995E−05 rs7582028 2 56492359 recessive  3.4708E−05 rs2606667 X 32118534 recessive 3.96933E−05 rs4762375 12 96187013 recessive 4.71254E−05 rs3123045 1 186014409 recessive 4.75117E−05 rs11259960 15 81379706 recessive 4.75345E−05 rs717680 19 36713838 recessive 4.88933E−05 rs7886752 X 108910208 recessive 4.93238E−05 rs5951221 X 71055374 recessive 5.05402E−05 rs1390753 19 36510231 recessive 5.22347E−05 rs2801003 20 51148981 recessive 5.96978E−05 rs201689 3 138935815 recessive 6.90308E−05 rs4466603 1 185974917 recessive 6.96757E−05 rs5956386 X 120973338 recessive 7.07813E−05 rs6625911 X 71025082 recessive 7.66867E−05 rs6837449 4 161485393 recessive 7.68408E−05 rs9527966 13 58786086 recessive 7.84219E−05 rs11729535 4 54892961 recessive 8.37881E−05 rs8132167 21 22412811 recessive  8.5729E−05 rs3904322 13 22266505 recessive 8.57459E−05 rs7882379 X 69161922 recessive 9.02044E−05 rs943005 6 50973778 recessive 9.03669E−05 rs2905345 7 22584245 recessive 9.52165E−05 rs9316187 13 45611702 recessive 9.81058E−05 rs12619445 2 146108819 Regression 1.27653E−07 rs2383666 1 185933373 Regression 1.76327E−06 rs179194 5 118969652 Regression 5.05518E−06 rs987237 6 50911008 Regression 1.36807E−05 rs9351362 6 94373609 Regression 1.51021E−05 rs7045961 9 91753818 Regression 1.53469E−05 rs10496375 2 103868516 Regression 1.66502E−05 rs280316 6 50733336 Regression 2.22013E−05 rs6775536 3 113609806 Regression 2.26455E−05 rs1337512 6 65250540 Regression 2.38015E−05 rs2863991 1 107350267 Regression 3.47629E−05 rs17567915 13 26367375 Regression  3.7007E−05 rs7582028 2 56492359 Regression 3.71298E−05 rs32652 5 118733443 Regression 3.86028E−05 rs7515340 1 41159457 Regression 4.06592E−05 rs7994380 13 38606949 Regression 4.09966E−05 rs2110037 1 185999482 Regression 4.13764E−05 rs10499542 7 22235869 Regression 4.48134E−05 rs2060805 4 38910802 Regression 4.95052E−05 rs201689 3 138935815 Regression 5.06905E−05 rs10148795 14 32915072 Regression 5.92498E−05 rs9867817 3 7880523 Regression 6.60519E−05 rs1953582 14 44076227 Regression 6.67784E−05 rs1386987 3 7880066 Regression 7.03677E−05 rs3753472 1 201398155 Regression 7.07942E−05 rs2718016 7 36051447 Regression 7.22882E−05 rs10509723 10 100268612 Regression 7.38044E−05 rs1978531 2 56597170 Regression 7.55069E−05 rs6954958 7 45249693 Regression 7.74875E−05 rs11679740 2 204023638 Regression 7.90284E−05 rs601902 1 55122610 Regression 8.14845E−05 rs4709364 6 160081214 Regression 8.28085E−05 rs6958502 7 5294954 Regression 8.82232E−05 rs6486762 12 128081919 Regression 9.75483E−05 rs2256209 1 153046167 Regression  9.7767E−05 rs10257067 7 69919396 Regression 9.86891E−05 rs9645958 13 73983467 Regression 9.94303E−05

TABLE 4 SNPs that are associated with weight loss in women without considering a diet-gene interaction. SNP Name Chromosome Position Analysis P Value rs12619445 2 146108819 additive 2.44823E−08 rs9840169 3 152204089 additive 3.20075E−07 rs7032088 9 77039544 additive 2.16456E−06 rs657152 9 135129085 additive 2.71777E−06 rs505922 9 135139049 additive 2.79645E−06 rs6763580 3 152197114 additive 5.82682E−06 rs1471910 18 20082257 additive 8.58634E−06 rs4680919 3 79108258 additive  9.1901E−06 rs1579404 4 168265709 additive  1.0954E−05 rs183766 5 75446386 additive 1.37926E−05 rs2594850 5 173200161 additive 1.38414E−05 rs4075894 7 263109 additive 1.89297E−05 rs11645312 16 25458336 additive 2.38664E−05 rs2501351 1 158082936 additive 2.76631E−05 rs10520079 5 129847936 additive 2.98362E−05 rs12478509 2 121365275 additive 3.06051E−05 rs717762 3 60574069 additive 3.06883E−05 rs4573201 7 118973303 additive 3.31294E−05 rs1885265 14 33000814 additive 3.34984E−05 rs2835380 21 36815958 additive 3.39231E−05 rs10519070 15 58856044 additive 3.75208E−05 rs39095 7 29253972 additive 3.94379E−05 rs4629935 9 77038449 additive 3.98487E−05 rs3786004 17 46428529 additive 4.93605E−05 rs245952 7 29240510 additive 5.19913E−05 rs4724062 7 41638064 additive 5.55627E−05 rs9672039 14 84645568 additive 5.63702E−05 rs563384 11 78186994 additive 6.04169E−05 rs11587479 1 7745671 additive 6.10147E−05 rs3788723 22 45284196 additive 6.26476E−05 rs11951994 5 128046771 additive  6.411E−05 rs9721194 8 789382 additive  6.9576E−05 rs735890 12 1624684 additive 7.32788E−05 rs937210 5 152929467 additive 8.11643E−05 rs6958502 7 5294954 additive 9.23225E−05 rs12619445 2 146108819 dominant 3.77137E−07 rs10520079 5 129847936 dominant 1.23332E−06 rs1362969 12 94539076 dominant 6.90287E−06 rs505922 9 135139049 dominant 8.79987E−06 rs657152 9 135129085 dominant  9.6712E−06 rs1471910 18 20082257 dominant 1.27844E−05 rs2594850 5 173200161 dominant 1.38414E−05 rs9840169 3 152204089 dominant 1.53497E−05 rs1579404 4 168265709 dominant 1.68947E−05 rs7840848 8 136114498 dominant 1.79112E−05 rs39095 7 29253972 dominant 2.16987E−05 rs1156672 2 56624351 dominant 2.27096E−05 rs7986369 13 47361123 dominant 2.40174E−05 rs3786004 17 46428529 dominant 2.58011E−05 rs12782802 10 71079848 dominant 2.86214E−05 rs3852738 16 81271051 dominant 3.20069E−05 rs2501351 1 158082936 dominant 3.25822E−05 rs2835380 21 36815958 dominant 3.27802E−05 rs1885265 14 33000814 dominant 3.34984E−05 rs3810415 19 1875652 dominant 3.35085E−05 rs7582028 2 56492359 dominant 3.67732E−05 rs4724062 7 41638064 dominant 3.80144E−05 rs1172377 10 98536449 dominant 3.81199E−05 rs2289043 4 96325344 dominant 3.97039E−05 rs380008 4 108428940 dominant 4.04727E−05 rs203738 4 108475811 dominant  4.2599E−05 rs11587479 1 7745671 dominant 4.29762E−05 rs735890 12 1624684 dominant 4.53907E−05 rs9341731 6 63917246 dominant  5.0097E−05 rs4034627 12 126963424 dominant 5.11319E−05 rs8078918 17 45685942 dominant 5.15181E−05 rs12068067 1 245092560 dominant 5.16583E−05 rs6461337 7 17701436 dominant 5.16923E−05 rs1551899 18 11543867 dominant 5.54213E−05 rs7503511 17 46366959 dominant 5.71549E−05 rs4243416 3 177076653 dominant  5.8692E−05 rs4680919 3 79108258 dominant 6.12466E−05 rs13749 14 101583979 dominant 6.16686E−05 rs4075894 7 263109 dominant 6.31174E−05 rs7739373 6 41036900 dominant 6.31672E−05 rs9672039 14 84645568 dominant 6.95171E−05 rs5906994 X 44298979 dominant 7.06601E−05 rs245952 7 29240510 dominant 7.44955E−05 rs1537295 9 77048125 dominant 7.55308E−05 rs11645312 16 25458336 dominant 7.62304E−05 rs4711987 6 51897547 dominant 7.65457E−05 rs9721194 8 789382 dominant 7.85628E−05 rs183766 5 75446386 dominant 8.07047E−05 rs2047948 7 9582932 dominant 8.08003E−05 rs3935608 1 245029641 dominant 8.60086E−05 rs4131907 3 72339004 dominant 8.68431E−05 rs4573201 7 118973303 dominant 8.70417E−05 rs6104657 20 10686949 dominant 9.80114E−05 rs1370911 18 22461690 dominant 9.88282E−05 rs7032088 9 77039544 recessive 4.02523E−06 rs6763580 3 152197114 recessive 1.30248E−05 rs10500921 11 22024111 recessive 1.47875E−05 rs9527966 13 58786086 recessive 2.68304E−05 rs1157094 11 96681103 recessive 3.02727E−05 rs12626044 20 1890717 recessive 3.07511E−05 rs11074699 16 25451951 recessive 3.20303E−05 rs6609428 X 37978857 recessive 4.07969E−05 rs8184900 21 42102167 recessive 4.24354E−05 rs3815854 2 220376981 recessive 4.33195E−05 rs963103 21 25950827 recessive 4.37124E−05 rs7314278 12 122945966 recessive 4.38269E−05 rs115327 17 34207526 recessive 4.43654E−05 rs12710711 2 19475965 recessive 4.59398E−05 rs9588664 13 89034295 recessive 4.94064E−05 rs2147289 10 7284212 recessive 5.09856E−05 rs9840169 3 152204089 recessive 5.30632E−05 rs17659728 5 150617325 recessive 5.95738E−05 rs13107698 4 117856182 recessive 5.99118E−05 rs6718973 2 56610360 recessive 6.57827E−05 rs11151405 18 63804153 recessive 6.68952E−05 rs3770305 2 144885646 recessive 6.73958E−05 rs11635733 15 68320485 recessive 7.13363E−05 rs4492387 8 123478740 recessive 7.18849E−05 rs2414458 15 54115535 recessive 7.69303E−05 rs1022454 16 25436943 recessive  7.8302E−05 rs9931136 16 25445488 recessive 7.95794E−05 rs11647919 16 25442789 recessive  8.1114E−05 rs1570613 13 30030711 recessive 8.24444E−05 rs7197258 16 25451072 recessive 8.47946E−05 rs1332349 9 77015552 recessive 8.91207E−05 rs17015937 2 79029178 recessive 9.17087E−05 rs10494146 1 112515883 recessive 9.37898E−05 rs537786 11 65251562 recessive 9.44806E−05 rs12619445 2 146108819 Regression 1.94558E−07 rs9840169 3 152204089 Regression 2.31867E−07 rs7032088 9 77039544 Regression 1.89435E−06 rs657152 9 135129085 Regression  3.8476E−06 rs6763580 3 152197114 Regression 3.91237E−06 rs505922 9 135139049 Regression  4.937E−06 rs12478509 2 121365275 Regression 6.99836E−06 rs4075894 7 263109 Regression 7.49734E−06 rs4680919 3 79108258 Regression 8.12512E−06 rs563384 11 78186994 Regression 1.02493E−05 rs2594850 5 173200161 Regression 1.09909E−05 rs1471910 18 20082257 Regression 1.28218E−05 rs10519070 15 58856044 Regression 2.53716E−05 rs4629935 9 77038449 Regression 2.65696E−05 rs1482900 5 105172732 Regression 3.35337E−05 rs1579404 4 168265709 Regression 3.42155E−05 rs1530634 11 45734100 Regression 3.42874E−05 rs717762 3 60574069 Regression  3.9088E−05 rs2467285 11 45733100 Regression  3.9305E−05 rs183766 5 75446386 Regression 4.42783E−05 rs4660492 1 41174729 Regression 5.20697E−05 rs3922389 8 129397684 Regression 5.74661E−05 rs7515340 1 41159457 Regression 5.90092E−05 rs203738 4 108475811 Regression 5.90752E−05 rs1172377 10 98536449 Regression 6.10487E−05 rs6958502 7 5294954 Regression 6.38476E−05 rs6802980 3 148877237 Regression 6.48684E−05 rs4131907 3 72339004 Regression 6.53775E−05 rs1001595 11 45738388 Regression 6.97166E−05 rs1957425 14 64428328 Regression 7.46703E−05 rs4466603 1 185974917 Regression 7.48969E−05 rs2110037 1 185999482 Regression 7.85313E−05 rs2501351 1 158082936 Regression 7.85722E−05 rs380008 4 108428940 Regression 7.97841E−05 rs3788723 22 45284196 Regression  9.3769E−05 rs11645312 16 25458336 Regression 9.45754E−05 rs12353712 X 67822668 additive   7.30E−05

TABLE 5 SNP markers including their nucleotide sequence. rs number MAF1 Gene name2 Nucleotide sequence rs606816 8.3 PRKACB TTGTGAGGGCCCTGTGCTA AGTACTC[A/C]TAATACA AAGACGCTAAGACACAAA (SEQ ID NO: 1) rs621750 8.5 PRKACB AGACTCTCAGGTTTTAGGC TTGAGCA[A/G]CAGGTTT GTATAGGGAGATAATAAG (SEQ ID NO: 2) rs655970 23.8 KMO; GCCTGGTGGCCTCTGTGAC OPN3 TAAGACT[C/T]ATGTGGA GACTAAACACCAGCTGTA (SEQ ID NO: 3) rs3771138 28.2 HPCAL1 GGAGTCTGTGGCTGCTGAC TGCTTTG[A/G]GTCTGTT TAAAAGGAAACGCAGCAT (SEQ ID NO: 4) rs3755264 28.5 HPCAL1; GAAGGAATGACTAAAGGAA AX748389 TTAGCGC[A/G]GAGCATG ACTGTAGGAGAACGCATG (SEQ ID NO: 5) rs876614 32.2 HPCAL1; CCACTGACCTAGAGCTCAT AX748389 TTCTGGA[A/G]GCCTTCC TGAAAGCTGTTGCATCAC (SEQ ID NO: 6) rs10496375 19.4 — TAAGTCAAGGAGTTTATAT CCTATTT[C/T]GGACAGC ATAAGAAAACATGAAAGG (SEQ ID NO: 7) rs9653448 28.6 — CTAAATAGTGTAATTGTGC AAAAGCC[A/G]AGAGGTA TTCAGTAATTGGTGTTTT (SEQ ID NO: 8) rs12478509 13.3 GLI2; GTGCACTGGGCGGGGTGTC hGli2 TGGTGGA[C/T]AAAGAAG AAATCGGGGAGCCTTGCT (SEQ ID NO: 9) rs12466364 26 ABI2 CAGAATGCTAAGGAAAACA GAATGAA[A/G]ACTAAAA AAAAGGAGAAAGGCAGAG (SEQ ID NO: 10) rs11679740 43.7 ABI2; AAGATAAAATAGGCCAACT argBPI GAAAAGA[A/G]CTCTGTT B;RAPH1 AGTGTTGCCAGTGAGGGC (SEQ ID NO: 11) rs2247380 40.1 RAPH1 CACTCTAGACCCATGGAAA TGGCTCT[A/G]ATAGCAC TCTGCTGTCCTGTATCTG (SEQ ID NO: 12) rs6751402 14.2 SP100 CTGGCTATTTATTTGGACA CACCGAT[C/T]AACACAT AGATACATGCTTCCAAGA (SEQ ID NO: 13) rs1559989 14.7 SP100 ATCGCAACATGCAGGCTCA AATGTAT[A/C]CATGCAA GACAATCAGTAAAACTCT (SEQ ID NO: 14) rs6442037 34.6 PTHR1 GCCCTCTAAAAAGCTTGTG CTGCTTT[A/G]TACTTAT ACAACACCAAGACCAACC (SEQ ID NO: 15) rs11719455 46.7 C3orf21 ACAAACCGGAGGCAAACAG CCCACAC[C/T]GAGAGGT AAGCAGTTTCTAACTGGG (SEQ ID NO: 16) rs183766 34.7 SV2C GGGAAAGGCATGACCAACT TAGGTTT[A/C]ACAATCA TCTTTCCTTTCAAAATAG (SEQ ID NO: 17) rs203142  16.6 KIAA1244 TGAGAGTAAAATTCCAGCT TCATAAT[A/G]ACATACT GATCCTGGGAGATCCTGC (SEQ ID NO: 18) rs6958502 16.7 SLC29A4; TCTGAGCCTTAGTTTCACC KIAA1856 ACATTGA[A/G]GAATTGG GCCAACTGACTTTGAGAT (SEQ ID NO: 19) rs3779341 11.4 MAGI2 TGAAAACAAAAACTGGCCT GTGAAGC[A/G]TAGAATT TCTCATTCTTCCTGCAAC (SEQ ID NO: 20) rs1019019 9.6 MAGI2 AGGCCAATGTGATTGACCA GAATTGT[A/G]ATTTATT TATTTAGTCTCATTTTTC (SEQ ID NO: 21) rs7831030 32.4 EYA1 TAACAGAACACTGACGCAT GGTCCAG[A/G]TTCTGCC CTCCTAAGAAGGCTACTC (SEQ ID NO: 22) rs920965 33.2 EYA1 TGAAGAAGTCCTTTAAAAA TGGGATC[A/C]TTTGATT CCAAATGTATTTATTAAA (SEQ ID NO: 23) rs657152 39.1 ABO TTCAAATGTTTTGCCTCCC ACGTTTC[G/T]GTTTCAA GAAGCTATTCGAGATAAA (SEQ ID NO: 24) rs505922 36.4 ABO GTCAAGATGTATCCAGCTG TACCTTT[C/T]ATGTGCG GTTTATTGTATACATCCG (SEQ ID NO: 25) rs3790033 48.4 GUCY1B2 CAAGTCTTGTTTGAATTGT CAGAGGC[A/G]TCAACCC AAATTGATGGAGTCAGGT (SEQ ID NO: 26) rs751539 10.2 GUCY1B2 CTGGACTGGATTTGCATCT GGGCTCT[A/G]CTGCTCA GAGCTGTGTGATCTTGAG (SEQ ID NO: 27) rs586788 24.6 DLGAP1 CTTTCTCTATTTTTTCATA CTTTGAG[A/C]GACACGA AATAGGAGCTTCAAACTA (SEQ ID NO: 28) rs7230740 33.7 DLGAP1 AAAGGCTGTCTTTTACACC TTTCATT[A/C]ACTAATT ATAGATTTCTGTCTCCCC (SEQ ID NO: 29) rs963103 26.2 JAM2 GTGTCAGAAGCTATTTCTG TGCTGGA[C/T]AACACTT TCTCCAAATACACTATGA (SEQ ID NO: 30) rs135557 45.7 PPARA CAGGAAAGGCAATCTGAAC CGGAGCC[C/T]GTGCAGC TGAGTGGAAGGGGGGCTG (SEQ ID NO: 31) rs8047814 34.5 CTRB2; AATCCATCAGCGTAATATG CTRB1 CATTAAT[C/T]GAATGAA AGGGGAAAATCACGTGAT (SEQ ID NO: 32) rs2084635 31.70 — AATGTCGGCTATAGGTTTG CGATAGA[C/T]AACTTAT ATTTTGAGGTATGCTCCT (SEQ ID NO: 33) rs11073977 36.70 — TGTTGAGCAAGCACTTACC TCTCCAC[A/C]CATGGAA AAATTTGAGAAACATAAC (SEQ ID NO: 34) rs928571 31.9 FLJ45557; AAAAGTGTTATTGGTACAT DOCK1 TCAGGTA[C/T]AAGTTAA ACAAGGCCAAAATATGCC (SEQ ID NO: 35) rs247979 30.8 NLGN1 TGTCAGAAAGCAAAGAGAA AGCAGAG[A/C]ATTAGTG ACACGGAACTGAGCAACT (SEQ ID NO: 36) rs4890647 8.7 RNF165 GCCATGTGGGGCCACAAGA GCAGGTT[C/T]AGCCGCA AGAAACAGTGACCCATGC (SEQ ID NO: 37) rs928534 6.7 NKAIN2 TGGCACAACCACCTAGATC CTGGCTT[A/G]ACTTATT CATCTGTAAAGCATCTGG (SEQ ID NO: 38) rs11058150 29.7 TMEM132B AATCTGTTGGGTGCGTTCT CCCGGTG[C/T]CCTGGAG GCTGCCAGCTCTATGTAG (SEQ ID NO: 39) rs11684785 30.0 TMEM163 TAGAGGGACCAGAACAAGC ATTCGAT[A/G]CCGCTAG AGTCCTGGAAAATGCCTT (SEQ ID NO: 40) rs6750788 30.0 TMEM163 AGCGCCCCTCAATGGGAGT AGTTAAA[A/G]GAAATAT TTGGGCCAGGCAGGGTAG (SEQ ID NO: 41) rs1471910 6.7 OSBPL1A AAGCCTCTAGTCTCTTGAC ATTATAC[A/G]TAAGTTC ATCCTGAGATTAGAAATG (SEQ ID NO: 42) rs6434276 31.2 GULP1; ATTAGAGGGTGTTTATGGA CED-6 TTGTAGT[A/C]TCTCAGA ATATAGTTTGAGAGAATT (SEQ ID NO: 43) rs10899257 14.2 GUCY2E CTCAAGTCCAGGGGCCACT CACGGGG[A/G]AATCATC TGACAGAGCAGCCTTTCT (SEQ ID NO: 44) rs2043448 42.4 NBEAL1 CAGTCAGAATTGGTGACTT TCAGAAA[A/G]TGAGGTA AATCTGTTGTCTGAGTCT (SEQ ID NO: 45) rs1515241 10.8 — TTATGATTGGCAGCAAGCA CTCCTAA[C/T]TGTTTTG TTTCTAGTGATAAGTTCA (SEQ ID NO: 46) rs12619445 21.7 — GTTAGAGGGTGGGGCTGAA AGTCCCA[A/C]ACCTCTA AACATGCCTTTGTGTTTC (SEQ ID NO: 47) rs2130858 16.1 — CACTCTCTAGTGAATGTGC CCATGCC[C/T]GAGGTTC GGCTCTCTGGTGAAGGAC (SEQ ID NO: 48) rs6718009 7.5 — GAAGCTAGTCAGAATCACA CTGCTAG[A/C]ACTCCAG GAGAATCTGTACTGTAAG (SEQ ID NO: 49) rs4492387 40.8 — CTTCGTTTGAGATCACCAC TCTTAAC[C/T]GCCTCCT GAGACTGTCTTCCCAGCT (SEQ ID NO: 50) rs11733026 23.3 LOC100131356 ATCATTTAACAGTCCTTGT TTATCAG[C/T]GATTGAA GGCATCCTTAGACATCTC (SEQ ID NO: 51) rs1109425 9.2 — AACGTAGTCTTCTACAGAG TTCAAAA[C/T]CCAGCAG CTTGCTTCTTCAAGGACA (SEQ ID NO: 52) rs3753472 30.0 ADORA1 AGAGGGAGGAGACTTCTAG CTACAGA[A/G]GTGGGTC CCTCCCCTCTTCTCTCCT (SEQ ID NO: 53) rs7355583 35.8 AGXT; TTCTATATTCTACGGATGT C2orf54 GCTGTTT[C/T]GTTGACT GACCTCTTATTTTCTCGT (SEQ ID NO: 54) rs7700981 5.8 ALDH7A1 AACCAAAAGATGCGATACA TTTTAGT[A/G]TTAGATG AATCAGCAGATTAGTGTA (SEQ ID NO: 55) rs10140366 12.5 ARF6 CTTTAAAAATCTCAAATGG TGTCAGG[C/T]GTGGTGG CTCACACCTGTAATCCCA (SEQ ID NO: 56) rs13220420 6.8 BCKBHD TTTAATTTATAAATTTGTT TTGGCTT[G/T]GTCTTTT TATCAGGGTAATAAATGC (SEQ ID NO: 57) rs6010669  16.7 LOC100132043 GACAAGAGCAGAGCACTGG   GGGTTCC[C/T]GCAGCCA GGCGACGTGGACGTTCTC (SEQ ID NO: 58) rs2428514  7.6 HCG22 CTCAAAACCTGGTAGATAG GTAAAGG[C/T]GATTGTC CAGAGGAAGTAACCAAGG (SEQ ID NO: 59) rs3731612 22.5 MARCO GTAGAGTGTATGATTTGAG AGAAATC[A/G]GTTACCT TCACCCTGTCCCCACCCC (SEQ ID NO: 60) rs10818023 7.5 — TTAACTCAAAGGTGAGAAG GGGGTGG[A/G]GGCAAGA TGAAAACTACTCTCCTTT (SEQ ID NO: 61) rs4691707 38.6 — TTTAATTGTCTACCCTTTA GATTTAT[A/G]TCATCCA ACTGTCTGCTTAATTATG (SEQ ID NO: 62) rs17407330 8.5 LOC100288911 TTGTAGGTAATTCTTAGAC TGCCCAA[A/G]GTAATAC ATTCTACTGGTGTTTTAC (SEQ ID NO: 63) rs1507081 5.8 FAM110B TTTTAATGCATCAGATCCT GTGCTAT[G/T]GTCTGTG TAAGAGAAAGGAACCCTG (SEQ ID NO: 64) rs12828607 7.5 TMEM117 TGTAGTGGGGCTAAGGTAG GGTATTC[A/G]GATTTCA TTCTAAAGTGCAATGCAC (SEQ ID NO: 65) rs12511535 36.7 — CATTTTTTACCTTTTTTGT TATCACC[C/T]TCACATG AGTATTTTTTAAGAAGAC (SEQ ID NO: 66) 1means minor allele frequency 2means the gene(s) in the vicinity of the SNP, genes determined as the −20 kb of the start codon and +20 kb of the stop codon of a gene

Claims

1. A method for predicting the likelihood of success of an individual in a dietary weight loss intervention program, the method comprising the steps of:

a) obtaining a biological sample comprising nucleic acid of the individual,
b) genotyping the nucleic acid for SNP rs183766 (SEQ ID NO:17), wherein the presence of SNP rs183766 is indicative of an increased likelihood of success of an individual in a dietary weight loss intervention program.

2. A method according to claim 1, characterized in that the individual is overweight or obese.

3. A method according to claim 1, characterized in that the dietary weight loss intervention program comprises subjecting the individual to a hypo-caloric diet.

4. A method according to claim 1, characterized in that the individual is a female.

5. A method according to claim 1, characterized in that the method further comprises the step of genotyping the nucleic acid for at least one SNP selected from the group of SNP markers as set forth in Tables 1 to 4.

6. A method according to claim 5, characterized in that the SNP is selected from the group consisting of rs7230740 (SEQ ID NO:29), rs751539 (SEQ ID NO:27), rs8047814 (SEQ ID NO:32), rs2084635 (SEQ ID NO:33), rs655970 (SEQ ID NO:3), rs621750 (SEQ ID NO:2), rs11073977 (SEQ ID NO:34), rs11679740 (SEQ ID NO:11), rs3755264 (SEQ ID NO:5), rs3771138 (SEQ ID NO:4), rs203142 (SEQ ID NO:18), rs6751402 (SEQ ID NO:13), rs6958502 (SEQ ID NO:19), rs505922 (SEQ ID NO:25), rs2247380 (SEQ ID NO:12), rs7230740 (SEQ ID NO:29), rs12466364 (SEQ ID NO:10), rs963103 (SEQ ID NO:30), rs6442037 (SEQ ID NO:15), rs1019019 (SEQ ID NO:21), rs928571 (SEQ ID NO:35), rs247979 (SEQ ID NO:36), rs11719455 (SEQ ID NO:16), rs4890647 (SEQ ID NO:37), rs657152 (SEQ ID NO:24), rs928534 (SEQ ID NO:38), rs11058150 (SEQ ID NO:39), rs3779341 (SEQ ID NO:20), rs11684785 (SEQ ID NO:40), rs6750788 (SEQ ID NO:41), rs1471910 (SEQ ID NO:42), rs6434276 (SEQ ID NO:43), rs7831030 (SEQ ID NO:22), rs10899257 (SEQ ID NO:44), rs876614 (SEQ ID NO:6), rs920965 (SEQ ID NO:23), rs2043448 (SEQ ID NO:45), rs1515241 (SEQ ID NO:46), rs12619445 (SEQ ID NO:47), rs2130858 (SEQ ID NO:48), rs6718009 (SEQ ID NO:49), rs4492387 (SEQ ID NO:50), rs11733026 (SEQ ID NO:51), rs1109425 (SEQ ID NO:52), rs3753472 (SEQ ID NO:53), rs7355583 (SEQ ID NO:54), rs7700981 (SEQ ID NO:55), rs10140366 (SEQ ID NO:56), rs13220420 (SEQ ID NO:57), rs6010669 (SEQ ID NO:58), rs2428514 (SEQ ID NO:59), rs3731612 (SEQ ID NO:60), rs10818023 (SEQ ID NO:61), rs4691707 (SEQ ID NO:62), rs17407330 (SEQ ID NO:63), rs1507081 (SEQ ID NO:64), rs12828607 (SEQ ID NO:65), rs12511535 (SEQ ID NO:66) and any combination thereof.

7. A kit for use in a method according to claim 1, the kit comprising at least one primer pair for genotyping SNP rs183766 (SEQ ID NO:17), and instructions explaining that detection of the presence of the SNP marker is indicative of a increased likelihood of success of an individual in a dietary weight loss intervention program.

8. A kit according to claim 7 further comprising at least one component selected from the group consisting of a restriction enzyme, a reverse transcriptase or polymerase, a positive control, a negative control, at least a further primer pair suitable for detecting (other) markers, an appropriate buffer for reverse transcription, a PCR and/or a hybridization reaction, a means used to label and a nucleotide mix for the PCR reaction.

9. A kit according to claim 7, characterized in that the further primer pair is a primer pair for genotyping at least one SNP selected from the group of SNP markers as set forth in Tables 1 to 4.

10. A kit according to claim 9, characterized in that the SNP is selected from the group consisting of rs7230740 (SEQ ID NO:29), rs751539 (SEQ ID NO:27), rs8047814 (SEQ ID NO:32), rs2084635 (SEQ ID NO:33), rs655970 (SEQ ID NO:3), rs621750 (SEQ ID NO:2), rs11073977 (SEQ ID NO:34), rs11679740 (SEQ ID NO:11), rs3755264 (SEQ ID NO:5), rs3771138 (SEQ ID NO:4), rs203142 (SEQ ID NO:18), rs6751402 (SEQ ID NO:13), rs6958502 (SEQ ID NO:19), rs505922 (SEQ ID NO:25), rs2247380 (SEQ ID NO:12), rs7230740 (SEQ ID NO:29), rs12466364 (SEQ ID NO:10), rs963103 (SEQ ID NO:30), rs6442037 (SEQ ID NO:15), rs1019019 (SEQ ID NO:21), rs928571 (SEQ ID NO:35), rs247979 (SEQ ID NO:36), rs11719455 (SEQ ID NO:16), rs4890647 (SEQ ID NO:37), rs657152 (SEQ ID NO:24), rs928534 (SEQ ID NO:38), rs11058150 (SEQ ID NO:39), rs3779341 (SEQ ID NO:20), rs11684785 (SEQ ID NO:40), rs6750788 (SEQ ID NO:41), rs1471910 (SEQ ID NO:42), rs6434276 (SEQ ID NO:43), rs7831030 (SEQ ID NO:22), rs10899257 (SEQ ID NO:44), rs876614 (SEQ ID NO:6), rs920965 (SEQ ID NO:23), rs2043448 (SEQ ID NO:45), rs1515241 (SEQ ID NO:46), rs12619445 (SEQ ID NO:47), rs2130858 (SEQ ID NO:48), rs6718009 (SEQ ID NO:49), rs4492387 (SEQ ID NO:50), rs11733026 (SEQ ID NO:51), rs1109425 (SEQ ID NO:52), rs3753472 (SEQ ID NO:53), rs7355583 (SEQ ID NO:54), rs7700981 (SEQ ID NO:55), rs10140366 (SEQ ID NO:56), rs13220420 (SEQ ID NO:57), rs6010669 (SEQ ID NO:58), rs2428514 (SEQ ID NO:59), rs3731612 (SEQ ID NO:60), rs10818023 (SEQ ID NO:61), rs4691707 (SEQ ID NO:62), rs17407330 (SEQ ID NO:63), rs1507081 (SEQ ID NO:64), rs12828607 (SEQ ID NO:65), rs12511535 (SEQ ID NO:66) and any combination thereof.

Patent History
Publication number: 20110159489
Type: Application
Filed: May 14, 2010
Publication Date: Jun 30, 2011
Applicant: DSM IP ASSETS B.V. (Heerlen)
Inventor: Özgür SANCAK (Basel)
Application Number: 12/780,297